siren pytorch

パイトーチのSIREN

SIREN の Pytorch 実装 - 定期的なアクティブ化機能を備えた暗黙的なニューラル表現

インストール

$ pip install siren-pytorch

使用法

SIRENベースの多層ニューラルネットワーク

 import torch
from torch import nn
from siren_pytorch import SirenNet

net = SirenNet (
    dim_in = 2 ,                        # input dimension, ex. 2d coor
    dim_hidden = 256 ,                  # hidden dimension
    dim_out = 3 ,                       # output dimension, ex. rgb value
    num_layers = 5 ,                    # number of layers
    final_activation = nn . Sigmoid (),   # activation of final layer (nn.Identity() for direct output)
    w0_initial = 30.                   # different signals may require different omega_0 in the first layer - this is a hyperparameter
)

coor = torch . randn ( 1 , 2 )
net ( coor ) # (1, 3) <- rgb value

1 つの SIREN レイヤー

 import torch
from siren_pytorch import Siren

neuron = Siren (
    dim_in = 3 ,
    dim_out = 256
)

coor = torch . randn ( 1 , 3 )
neuron ( coor ) # (1, 256)

Sine アクティベーション ( torch.sinの単なるラッパー)

 import torch
from siren_pytorch import Sine

act = Sine ( 1. )
coor = torch . randn ( 1 , 2 )
act ( coor )

指定されたSirenNetからの指定された高さと幅の特定の画像でトレーニングし、その後生成するラッパー。

 import torch
from torch import nn
from siren_pytorch import SirenNet , SirenWrapper

net = SirenNet (
    dim_in = 2 ,                        # input dimension, ex. 2d coor
    dim_hidden = 256 ,                  # hidden dimension
    dim_out = 3 ,                       # output dimension, ex. rgb value
    num_layers = 5 ,                    # number of layers
    w0_initial = 30.                   # different signals may require different omega_0 in the first layer - this is a hyperparameter
)

wrapper = SirenWrapper (
    net ,
    image_width = 256 ,
    image_height = 256
)

img = torch . randn ( 1 , 3 , 256 , 256 )
loss = wrapper ( img )
loss . backward ()

# after much training ...
# simply invoke the wrapper without passing in anything

pred_img = wrapper () # (1, 3, 256, 256) 

潜在コードによる変調

新しい論文では、潜在コードでサイレンを調整する最良の方法は、各層の隠れ状態が要素ごとにサイレンの対応する層と乗算される変調器フィードフォワード ネットワークに潜在ベクトルを渡すことであると提案しています。

これは、 SirenWrapperで追加のキーワードlatent_dim設定するだけで使用できます。

 import torch
from torch import nn
from siren_pytorch import SirenNet , SirenWrapper

net = SirenNet (
    dim_in = 2 ,                        # input dimension, ex. 2d coor
    dim_hidden = 256 ,                  # hidden dimension
    dim_out = 3 ,                       # output dimension, ex. rgb value
    num_layers = 5 ,                    # number of layers
    w0_initial = 30.                   # different signals may require different omega_0 in the first layer - this is a hyperparameter
)

wrapper = SirenWrapper (
    net ,
    latent_dim = 512 ,
    image_width = 256 ,
    image_height = 256
)

latent = nn . Parameter ( torch . zeros ( 512 ). normal_ ( 0 , 1e-2 ))
img = torch . randn ( 1 , 3 , 256 , 256 )

loss = wrapper ( img , latent = latent )
loss . backward ()

# after much training ...
# simply invoke the wrapper without passing in anything

pred_img = wrapper ( latent = latent ) # (1, 3, 256, 256) 

引用

 @misc { sitzmann2020implicit ,
    title   = { Implicit Neural Representations with Periodic Activation Functions } ,
    author  = { Vincent Sitzmann and Julien N. P. Martel and Alexander W. Bergman and David B. Lindell and Gordon Wetzstein } ,
    year    = { 2020 } ,
    eprint  = { 2006.09661 } ,
    archivePrefix = { arXiv } ,
    primaryClass = { cs.CV }
}

 @misc { mehta2021modulated ,
    title   = { Modulated Periodic Activations for Generalizable Local Functional Representations } , 
    author  = { Ishit Mehta and Michaël Gharbi and Connelly Barnes and Eli Shechtman and Ravi Ramamoorthi and Manmohan Chandraker } ,
    year    = { 2021 } ,
    eprint  = { 2104.03960 } ,
    archivePrefix = { arXiv } ,
    primaryClass = { cs.CV }
}