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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.
"""Neural Volumes decoder """
import math
from typing import Optional, Dict, List
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import models.utils
from models.utils import LinearELR, ConvTranspose2dELR, ConvTranspose3dELR
class Reshape(nn.Module):
def __init__(self, *args):
super(Reshape, self).__init__()
self.shape = args
def forward(self, x):
return x.view(self.shape)
class ContentDecoder(nn.Module):
def __init__(self, primsize, inch, outch, chstart=256, hstart=4,
texwarp=False, elr=True, norm=None, mod=False, ub=True, upconv=None,
penultch=None):
super(ContentDecoder, self).__init__()
assert not texwarp
assert upconv == None
self.primsize = primsize
nlayers = int(math.log2(self.primsize / hstart))
lastch = chstart
dims = (hstart, hstart, hstart)
layers = []
layers.append(LinearELR(inch, chstart*dims[0]*dims[1]*dims[2], act=nn.LeakyReLU(0.2)))
layers.append(Reshape(-1, chstart, dims[0], dims[1], dims[2]))
for i in range(nlayers):
nextch = lastch if i % 2 == 0 else lastch // 2
if i == nlayers - 2 and penultch is not None:
nextch = penultch
layers.append(ConvTranspose3dELR(
lastch,
(outch if i == nlayers - 1 else nextch),
4, 2, 1,
ub=(dims[0]*2, dims[1]*2, dims[2]*2) if ub else None,
norm=None if i == nlayers - 1 else norm,
act=None if i == nlayers - 1 else nn.LeakyReLU(0.2)
))
lastch = nextch
dims = (dims[0] * 2, dims[1] * 2, dims[2] * 2)
self.mod = nn.Sequential(*layers)
def forward(self, enc, renderoptions : Dict[str, str], trainiter : Optional[int]=None):
x = self.mod(enc)
algo = renderoptions.get("algo")
chlast = renderoptions.get("chlast")
if chlast is not None and bool(chlast):
# reorder channels last
outch = x.size(1)
x = x.permute(0, 2, 3, 4, 1)[:, None, :, :, :, :].contiguous()
else:
outch = x.size(1)
x = x[:, None, :, :, :, :].contiguous()
return x
def get_dec(dectype, **kwargs):
if dectype == "conv":
return ContentDecoder(**kwargs)
else:
raise
class Decoder(nn.Module):
def __init__(self,
volradius,
dectype="conv",
primsize=128,
chstart=256,
penultch=None,
condsize=0,
warptype="conv",
warpprimsize=32,
sharedrgba=False,
norm=None,
mod=False,
elr=True,
notplateact=False,
postrainstart=-1,
alphatrainstart=-1,
renderoptions={},
**kwargs):
"""
Parameters
----------
volradius : float
radius of bounding volume of scene
dectype : string
type of content decoder, options are "slab2d", "slab2d3d", "slab2d3dv2"
primsize : Tuple[int, int, int]
size of primitive dimensions
postrainstart : int
training iterations to start learning position, rotation, and
scaling (i.e., primitives stay frozen until this iteration number)
condsize : int
unused
motiontype : string
motion model, options are "linear" and "deconv"
warptype : string
warp model, options are "same" to use same architecture as content
or None
sharedrgba : bool
True to use 1 branch to output rgba, False to use 1 branch for rgb
and 1 branch for alpha
"""
super(Decoder, self).__init__()
self.volradius = volradius
self.postrainstart = postrainstart
self.alphatrainstart = alphatrainstart
self.primsize = primsize
self.warpprimsize = warpprimsize
self.notplateact = notplateact
self.enc = LinearELR(256 + condsize, 256)
# slab decoder (RGBA)
if sharedrgba:
self.rgbadec = get_dec(dectype, primsize=primsize,
inch=256+3, outch=4, norm=norm, mod=mod, elr=elr,
penultch=penultch, **kwargs)
if renderoptions.get("half", False):
self.rgbadec = self.rgbadec.half()
if renderoptions.get("chlastconv", False):
self.rgbadec = self.rgbadec.to(memory_format=torch.channels_last)
else:
self.rgbdec = get_dec(dectype, primsize=primsize,
inch=256+3, outch=3, chstart=chstart, norm=norm, mod=mod,
elr=elr, penultch=penultch, **kwargs)
self.alphadec = get_dec(dectype, primsize=primsize,
inch=256, outch=1, chstart=chstart, norm=norm, mod=mod,
elr=elr, penultch=penultch, **kwargs)
self.rgbadec = None
if renderoptions.get("half", False):
self.rgbdec = self.rgbdec.half()
self.alphadec = self.alphadec.half()
if renderoptions.get("chlastconv", False):
self.rgbdec = self.rgbdec.to(memory_format=torch.channels_last)
self.alphadec = self.alphadec.to(memory_format=torch.channels_last)
# warp field decoder
if warptype is not None:
self.warpdec = get_dec(warptype, primsize=warpprimsize,
inch=256, outch=3, chstart=chstart, norm=norm, mod=mod, elr=elr, **kwargs)
else:
self.warpdec = None
def forward(self,
encoding,
viewpos,
condinput : Optional[torch.Tensor]=None,
renderoptions : Optional[Dict[str, str]]=None,
trainiter : int=-1,
evaliter : Optional[torch.Tensor]=None,
losslist : Optional[List[str]]=None,
modelmatrix : Optional[torch.Tensor]=None):
"""
Parameters
----------
encoding : torch.Tensor [B, 256]
Encoding of current frame
viewpos : torch.Tensor [B, 3]
Viewing position of target camera view
condinput : torch.Tensor [B, ?]
Additional conditioning input (e.g., headpose)
renderoptions : dict
Options for rendering (e.g., rendering debug images)
trainiter : int,
Current training iteration
losslist : list,
List of losses to compute and return
Returns
-------
result : dict,
Contains predicted vertex positions, primitive contents and
locations, scaling, and orientation, and any losses.
"""
assert renderoptions is not None
assert losslist is not None
if condinput is not None:
encoding = torch.cat([encoding, condinput], dim=1)
encoding = self.enc(encoding)
viewdirs = F.normalize(viewpos, dim=1)
primpos = torch.zeros(encoding.size(0), 1, 3, device=encoding.device)
primrot = torch.eye(3, device=encoding.device)[None, None, :, :].repeat(encoding.size(0), 1, 1, 1)
primscale = torch.ones(encoding.size(0), 1, 3, device=encoding.device)
# options
algo = renderoptions.get("algo")
chlast = renderoptions.get("chlast")
half = renderoptions.get("half")
if self.rgbadec is not None:
# shared rgb and alpha branch
scale = torch.tensor([25., 25., 25., 1.], device=encoding.device)
bias = torch.tensor([100., 100., 100., 0.], device=encoding.device)
if chlast is not None and bool(chlast):
scale = scale[None, None, None, None, None, :]
bias = bias[None, None, None, None, None, :]
else:
scale = scale[None, None, :, None, None, None]
bias = bias[None, None, :, None, None, None]
templatein = torch.cat([encoding, viewdirs], dim=1)
if half is not None and bool(half):
templatein = templatein.half()
template = self.rgbadec(templatein, trainiter=trainiter, renderoptions=renderoptions)
template = bias + scale * template
if not self.notplateact:
template = F.relu(template)
if half is not None and bool(half):
template = template.float()
else:
templatein = torch.cat([encoding, viewdirs], dim=1)
if half is not None and bool(half):
templatein = templatein.half()
primrgb = self.rgbdec(templatein, trainiter=trainiter, renderoptions=renderoptions)
primrgb = primrgb * 25. + 100.
if not self.notplateact:
primrgb = F.relu(primrgb)
templatein = encoding
if half is not None and bool(half):
templatein = templatein.half()
primalpha = self.alphadec(templatein, trainiter=trainiter, renderoptions=renderoptions)
if not self.notplateact:
primalpha = F.relu(primalpha)
if trainiter <= self.alphatrainstart:
primalpha = primalpha * 0. + 1.
if algo is not None and int(algo) == 4:
template = torch.cat([primrgb, primalpha], dim=-1)
elif chlast is not None and bool(chlast):
template = torch.cat([primrgb, primalpha], dim=-1)
else:
template = torch.cat([primrgb, primalpha], dim=2)
if half is not None and bool(half):
template = template.float()
if self.warpdec is not None:
warp = self.warpdec(encoding, trainiter=trainiter, renderoptions=renderoptions) * 0.01
warp = warp + torch.stack(torch.meshgrid(
torch.linspace(-1., 1., self.warpprimsize, device=encoding.device),
torch.linspace(-1., 1., self.warpprimsize, device=encoding.device),
torch.linspace(-1., 1., self.warpprimsize, device=encoding.device))[::-1],
dim=-1 if chlast is not None and bool(chlast) else 0)[None, None, :, :, :, :]
warp = warp.contiguous()
else:
warp = None
losses = {}
# prior on primitive volume
if "primvolsum" in losslist:
losses["primvolsum"] = torch.sum(torch.prod(1. / primscale, dim=-1), dim=-1)
if "logprimscalevar" in losslist:
logprimscale = torch.log(primscale)
logprimscalemean = torch.mean(logprimscale, dim=1, keepdim=True)
losses["logprimscalevar"] = torch.mean((logprimscale - logprimscalemean) ** 2)
result = {
"template": template,
"primpos": primpos,
"primrot": primrot,
"primscale": primscale}
if warp is not None:
result["warp"] = warp
return result, losses
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