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from dataclasses import dataclass, field
from typing import Callable, List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from jaxtyping import Float
from torch import Tensor
from torch.amp import custom_bwd, custom_fwd
from torch.autograd import Function
from spar3d.models.utils import BaseModule, normalize
from spar3d.utils import get_device
def conditional_decorator(decorator_with_args, condition, *args, **kwargs):
def wrapper(fn):
if condition:
if len(kwargs) == 0:
return decorator_with_args
return decorator_with_args(*args, **kwargs)(fn)
else:
return fn
return wrapper
class PixelShuffleUpsampleNetwork(BaseModule):
@dataclass
class Config(BaseModule.Config):
in_channels: int = 1024
out_channels: int = 40
scale_factor: int = 4
conv_layers: int = 4
conv_kernel_size: int = 3
cfg: Config
def configure(self) -> None:
layers = []
output_channels = self.cfg.out_channels * self.cfg.scale_factor**2
in_channels = self.cfg.in_channels
for i in range(self.cfg.conv_layers):
cur_out_channels = (
in_channels if i != self.cfg.conv_layers - 1 else output_channels
)
layers.append(
nn.Conv2d(
in_channels,
cur_out_channels,
self.cfg.conv_kernel_size,
padding=(self.cfg.conv_kernel_size - 1) // 2,
)
)
if i != self.cfg.conv_layers - 1:
layers.append(nn.ReLU(inplace=True))
layers.append(nn.PixelShuffle(self.cfg.scale_factor))
self.upsample = nn.Sequential(*layers)
def forward(
self, triplanes: Float[Tensor, "B 3 Ci Hp Wp"]
) -> Float[Tensor, "B 3 Co Hp2 Wp2"]:
return rearrange(
self.upsample(
rearrange(triplanes, "B Np Ci Hp Wp -> (B Np) Ci Hp Wp", Np=3)
),
"(B Np) Co Hp Wp -> B Np Co Hp Wp",
Np=3,
)
class _TruncExp(Function): # pylint: disable=abstract-method
# Implementation from torch-ngp:
# https://github.com/ashawkey/torch-ngp/blob/93b08a0d4ec1cc6e69d85df7f0acdfb99603b628/activation.py
@staticmethod
@conditional_decorator(
custom_fwd,
"cuda" in get_device(),
cast_inputs=torch.float32,
device_type="cuda",
)
def forward(ctx, x): # pylint: disable=arguments-differ
ctx.save_for_backward(x)
return torch.exp(x)
@staticmethod
@conditional_decorator(custom_bwd, "cuda" in get_device())
def backward(ctx, g): # pylint: disable=arguments-differ
x = ctx.saved_tensors[0]
return g * torch.exp(torch.clamp(x, max=15))
trunc_exp = _TruncExp.apply
def get_activation(name) -> Callable:
if name is None:
return lambda x: x
name = name.lower()
if name == "none" or name == "linear" or name == "identity":
return lambda x: x
elif name == "lin2srgb":
return lambda x: torch.where(
x > 0.0031308,
torch.pow(torch.clamp(x, min=0.0031308), 1.0 / 2.4) * 1.055 - 0.055,
12.92 * x,
).clamp(0.0, 1.0)
elif name == "exp":
return lambda x: torch.exp(x)
elif name == "shifted_exp":
return lambda x: torch.exp(x - 1.0)
elif name == "trunc_exp":
return trunc_exp
elif name == "shifted_trunc_exp":
return lambda x: trunc_exp(x - 1.0)
elif name == "sigmoid":
return lambda x: torch.sigmoid(x)
elif name == "tanh":
return lambda x: torch.tanh(x)
elif name == "shifted_softplus":
return lambda x: F.softplus(x - 1.0)
elif name == "scale_-11_01":
return lambda x: x * 0.5 + 0.5
elif name == "negative":
return lambda x: -x
elif name == "normalize_channel_last":
return lambda x: normalize(x)
elif name == "normalize_channel_first":
return lambda x: normalize(x, dim=1)
else:
try:
return getattr(F, name)
except AttributeError:
raise ValueError(f"Unknown activation function: {name}")
class LambdaModule(torch.nn.Module):
def __init__(self, lambd: Callable[[torch.Tensor], torch.Tensor]):
super().__init__()
self.lambd = lambd
def forward(self, x):
return self.lambd(x)
def get_activation_module(name) -> torch.nn.Module:
return LambdaModule(get_activation(name))
@dataclass
class HeadSpec:
name: str
out_channels: int
n_hidden_layers: int
output_activation: Optional[str] = None
out_bias: float = 0.0
class MaterialMLP(BaseModule):
@dataclass
class Config(BaseModule.Config):
in_channels: int = 120
n_neurons: int = 64
activation: str = "silu"
heads: List[HeadSpec] = field(default_factory=lambda: [])
cfg: Config
def configure(self) -> None:
assert len(self.cfg.heads) > 0
heads = {}
for head in self.cfg.heads:
head_layers = []
for i in range(head.n_hidden_layers):
head_layers += [
nn.Linear(
self.cfg.in_channels if i == 0 else self.cfg.n_neurons,
self.cfg.n_neurons,
),
self.make_activation(self.cfg.activation),
]
head_layers += [
nn.Linear(
self.cfg.n_neurons,
head.out_channels,
),
]
heads[head.name] = nn.Sequential(*head_layers)
self.heads = nn.ModuleDict(heads)
def make_activation(self, activation):
if activation == "relu":
return nn.ReLU(inplace=True)
elif activation == "silu":
return nn.SiLU(inplace=True)
else:
raise NotImplementedError
def keys(self):
return self.heads.keys()
def forward(
self, x, include: Optional[List] = None, exclude: Optional[List] = None
):
if include is not None and exclude is not None:
raise ValueError("Cannot specify both include and exclude.")
if include is not None:
heads = [h for h in self.cfg.heads if h.name in include]
elif exclude is not None:
heads = [h for h in self.cfg.heads if h.name not in exclude]
else:
heads = self.cfg.heads
out = {
head.name: get_activation(head.output_activation)(
self.heads[head.name](x) + head.out_bias
)
for head in heads
}
return out
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