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| """ | |
| This file defines the core research contribution | |
| """ | |
| import math | |
| import torch | |
| from torch import nn | |
| from models.stylegan2.model import Generator | |
| from models.hyperstyle.configs.paths_config import model_paths | |
| from models.hyperstyle.encoders import restyle_e4e_encoders | |
| from models.hyperstyle.utils.resnet_mapping import RESNET_MAPPING | |
| class e4e(nn.Module): | |
| def __init__(self, opts): | |
| super(e4e, self).__init__() | |
| self.set_opts(opts) | |
| self.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, channel_multiplier=2) | |
| self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256)) | |
| # Load weights if needed | |
| self.load_weights() | |
| def set_encoder(self): | |
| if self.opts.encoder_type == 'ProgressiveBackboneEncoder': | |
| encoder = restyle_e4e_encoders.ProgressiveBackboneEncoder(50, 'ir_se', self.n_styles, self.opts) | |
| elif self.opts.encoder_type == 'ResNetProgressiveBackboneEncoder': | |
| encoder = restyle_e4e_encoders.ResNetProgressiveBackboneEncoder(self.n_styles, self.opts) | |
| else: | |
| raise Exception(f'{self.opts.encoder_type} is not a valid encoders') | |
| return encoder | |
| def load_weights(self): | |
| if self.opts.checkpoint_path is not None: | |
| print(f'Loading ReStyle e4e from checkpoint: {self.opts.checkpoint_path}') | |
| ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu') | |
| self.encoder.load_state_dict(self.__get_keys(ckpt, 'encoder'), strict=True) | |
| self.decoder.load_state_dict(self.__get_keys(ckpt, 'decoder'), strict=True) | |
| self.__load_latent_avg(ckpt) | |
| else: | |
| encoder_ckpt = self.__get_encoder_checkpoint() | |
| self.encoder.load_state_dict(encoder_ckpt, strict=False) | |
| print(f'Loading decoder weights from pretrained path: {self.opts.stylegan_weights}') | |
| ckpt = torch.load(self.opts.stylegan_weights) | |
| self.decoder.load_state_dict(ckpt['g_ema'], strict=True) | |
| self.__load_latent_avg(ckpt, repeat=self.n_styles) | |
| def forward(self, x, latent=None, resize=True, input_code=False, randomize_noise=True, | |
| return_latents=False, average_code=False, input_is_full=False): | |
| if input_code: | |
| codes = x | |
| else: | |
| codes = self.encoder(x) | |
| # residual step | |
| if x.shape[1] == 6 and latent is not None: | |
| # learn error with respect to previous iteration | |
| codes = codes + latent | |
| else: | |
| # first iteration is with respect to the avg latent code | |
| codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1) | |
| if average_code: | |
| input_is_latent = True | |
| else: | |
| input_is_latent = (not input_code) or (input_is_full) | |
| 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 | |
| def __get_encoder_checkpoint(self): | |
| if "ffhq" in self.opts.dataset_type: | |
| print('Loading encoders weights from irse50!') | |
| encoder_ckpt = torch.load(model_paths['ir_se50']) | |
| # Transfer the RGB input of the irse50 network to the first 3 input channels of pSp's encoder | |
| if self.opts.input_nc != 3: | |
| shape = encoder_ckpt['input_layer.0.weight'].shape | |
| altered_input_layer = torch.randn(shape[0], self.opts.input_nc, shape[2], shape[3], dtype=torch.float32) | |
| altered_input_layer[:, :3, :, :] = encoder_ckpt['input_layer.0.weight'] | |
| encoder_ckpt['input_layer.0.weight'] = altered_input_layer | |
| return encoder_ckpt | |
| else: | |
| print('Loading encoders weights from resnet34!') | |
| encoder_ckpt = torch.load(model_paths['resnet34']) | |
| # Transfer the RGB input of the resnet34 network to the first 3 input channels of pSp's encoder | |
| if self.opts.input_nc != 3: | |
| shape = encoder_ckpt['conv1.weight'].shape | |
| altered_input_layer = torch.randn(shape[0], self.opts.input_nc, shape[2], shape[3], dtype=torch.float32) | |
| altered_input_layer[:, :3, :, :] = encoder_ckpt['conv1.weight'] | |
| encoder_ckpt['conv1.weight'] = altered_input_layer | |
| mapped_encoder_ckpt = dict(encoder_ckpt) | |
| for p, v in encoder_ckpt.items(): | |
| for original_name, psp_name in RESNET_MAPPING.items(): | |
| if original_name in p: | |
| mapped_encoder_ckpt[p.replace(original_name, psp_name)] = v | |
| mapped_encoder_ckpt.pop(p) | |
| return encoder_ckpt | |
| 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 | |