Spaces:
Sleeping
Sleeping
File size: 3,599 Bytes
8e0b903 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 |
from dataclasses import dataclass
from typing import Optional
import torch
import torch.nn as nn
from einops import rearrange
from x3D_utils import BaseModule
class TriplaneUpsampleNetwork(BaseModule):
@dataclass
class Config(BaseModule.Config):
in_channels: int
out_channels: int
cfg: Config
def configure(self) -> None:
self.upsample = nn.ConvTranspose2d(
self.cfg.in_channels, self.cfg.out_channels, kernel_size=2, stride=2
)
def forward(self, triplanes: torch.Tensor) -> torch.Tensor:
triplanes_up = 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,
)
return triplanes_up
class NeRFMLP(BaseModule):
@dataclass
class Config(BaseModule.Config):
in_channels: int
n_neurons: int
n_hidden_layers: int
activation: str = "relu"
bias: bool = True
weight_init: Optional[str] = "kaiming_uniform"
bias_init: Optional[str] = None
cfg: Config
def configure(self) -> None:
layers = [
self.make_linear(
self.cfg.in_channels,
self.cfg.n_neurons,
bias=self.cfg.bias,
weight_init=self.cfg.weight_init,
bias_init=self.cfg.bias_init,
),
self.make_activation(self.cfg.activation),
]
for i in range(self.cfg.n_hidden_layers - 1):
layers += [
self.make_linear(
self.cfg.n_neurons,
self.cfg.n_neurons,
bias=self.cfg.bias,
weight_init=self.cfg.weight_init,
bias_init=self.cfg.bias_init,
),
self.make_activation(self.cfg.activation),
]
layers += [
self.make_linear(
self.cfg.n_neurons,
4, # density 1 + features 3
bias=self.cfg.bias,
weight_init=self.cfg.weight_init,
bias_init=self.cfg.bias_init,
)
]
self.layers = nn.Sequential(*layers)
def make_linear(
self,
dim_in,
dim_out,
bias=True,
weight_init=None,
bias_init=None,
):
layer = nn.Linear(dim_in, dim_out, bias=bias)
if weight_init is None:
pass
elif weight_init == "kaiming_uniform":
torch.nn.init.kaiming_uniform_(layer.weight, nonlinearity="relu")
else:
raise NotImplementedError
if bias:
if bias_init is None:
pass
elif bias_init == "zero":
torch.nn.init.zeros_(layer.bias)
else:
raise NotImplementedError
return layer
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 forward(self, x):
inp_shape = x.shape[:-1]
x = x.reshape(-1, x.shape[-1])
features = self.layers(x)
features = features.reshape(*inp_shape, -1)
out = {"density": features[..., 0:1], "features": features[..., 1:4]}
return out
|