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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| from typing import Optional, List | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from models.utils import LinearELR, Conv2dELR | |
| class Encoder(torch.nn.Module): | |
| def __init__(self, latentdim=256, hiq=True, texin=True, | |
| conv=Conv2dELR, lin=LinearELR, | |
| demod=True, texsize=1024, vertsize=21918): | |
| super(Encoder, self).__init__() | |
| self.latentdim = latentdim | |
| self.vertbranch = lin(vertsize, 256, norm="demod", act=nn.LeakyReLU(0.2)) | |
| if texin: | |
| cm = 2 if hiq else 1 | |
| layers = [] | |
| chout = 128*cm | |
| chin = 128*cm | |
| nlayers = int(np.log2(texsize)) - 2 | |
| for i in range(nlayers): | |
| if i == nlayers - 1: | |
| chin = 3 | |
| layers.append( | |
| conv(chin, chout, 4, 2, 1, norm="demod" if demod else None, act=nn.LeakyReLU(0.2))) | |
| if chin == chout: | |
| chin = chout // 2 | |
| else: | |
| chout = chin | |
| self.texbranch1 = nn.Sequential(*(layers[::-1])) | |
| self.texbranch2 = lin(cm*128*4*4, 256, norm="demod", act=nn.LeakyReLU(0.2)) | |
| self.mu = lin(512, self.latentdim) | |
| self.logstd = lin(512, self.latentdim) | |
| else: | |
| self.mu = lin(256, self.latentdim) | |
| self.logstd = lin(256, self.latentdim) | |
| def forward(self, verts, texture : Optional[torch.Tensor]=None, losslist : Optional[List[str]]=None): | |
| assert losslist is not None | |
| x = self.vertbranch(verts.view(verts.size(0), -1)) | |
| if texture is not None: | |
| texture = self.texbranch1(texture).reshape(verts.size(0), -1) | |
| texture = self.texbranch2(texture) | |
| x = torch.cat([x, texture], dim=1) | |
| mu, logstd = self.mu(x) * 0.1, self.logstd(x) * 0.01 | |
| if self.training: | |
| z = mu + torch.exp(logstd) * torch.randn_like(logstd) | |
| else: | |
| z = mu | |
| losses = {} | |
| if "kldiv" in losslist: | |
| losses["kldiv"] = torch.mean(-0.5 - logstd + 0.5 * mu ** 2 + 0.5 * torch.exp(2 * logstd), dim=-1) | |
| return {"encoding": z}, losses | |