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