mirror of
https://github.com/SillyTavern/SillyTavern-Extras.git
synced 2026-03-12 06:50:09 +00:00
127 lines
3.7 KiB
Python
127 lines
3.7 KiB
Python
from abc import ABC, abstractmethod
|
|
from typing import Optional
|
|
|
|
import torch
|
|
from torch import layer_norm
|
|
from torch.nn import Module, BatchNorm2d, InstanceNorm2d, Parameter
|
|
from torch.nn.init import normal_, constant_
|
|
|
|
from tha3.nn.pass_through import PassThrough
|
|
|
|
|
|
class PixelNormalization(Module):
|
|
def __init__(self, epsilon=1e-8):
|
|
super().__init__()
|
|
self.epsilon = epsilon
|
|
|
|
def forward(self, x):
|
|
return x / torch.sqrt((x ** 2).mean(dim=1, keepdim=True) + self.epsilon)
|
|
|
|
|
|
class NormalizationLayerFactory(ABC):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@abstractmethod
|
|
def create(self, num_features: int, affine: bool = True) -> Module:
|
|
pass
|
|
|
|
@staticmethod
|
|
def resolve_2d(factory: Optional['NormalizationLayerFactory']) -> 'NormalizationLayerFactory':
|
|
if factory is None:
|
|
return InstanceNorm2dFactory()
|
|
else:
|
|
return factory
|
|
|
|
|
|
class Bias2d(Module):
|
|
def __init__(self, num_features: int):
|
|
super().__init__()
|
|
self.num_features = num_features
|
|
self.bias = Parameter(torch.zeros(1, num_features, 1, 1))
|
|
|
|
def forward(self, x):
|
|
return x + self.bias
|
|
|
|
|
|
class NoNorm2dFactory(NormalizationLayerFactory):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def create(self, num_features: int, affine: bool = True) -> Module:
|
|
if affine:
|
|
return Bias2d(num_features)
|
|
else:
|
|
return PassThrough()
|
|
|
|
|
|
class BatchNorm2dFactory(NormalizationLayerFactory):
|
|
def __init__(self,
|
|
weight_mean: Optional[float] = None,
|
|
weight_std: Optional[float] = None,
|
|
bias: Optional[float] = None):
|
|
super().__init__()
|
|
self.bias = bias
|
|
self.weight_std = weight_std
|
|
self.weight_mean = weight_mean
|
|
|
|
def get_weight_mean(self):
|
|
if self.weight_mean is None:
|
|
return 1.0
|
|
else:
|
|
return self.weight_mean
|
|
|
|
def get_weight_std(self):
|
|
if self.weight_std is None:
|
|
return 0.02
|
|
else:
|
|
return self.weight_std
|
|
|
|
def create(self, num_features: int, affine: bool = True) -> Module:
|
|
module = BatchNorm2d(num_features=num_features, affine=affine)
|
|
if affine:
|
|
if self.weight_mean is not None or self.weight_std is not None:
|
|
normal_(module.weight, self.get_weight_mean(), self.get_weight_std())
|
|
if self.bias is not None:
|
|
constant_(module.bias, self.bias)
|
|
return module
|
|
|
|
|
|
class InstanceNorm2dFactory(NormalizationLayerFactory):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def create(self, num_features: int, affine: bool = True) -> Module:
|
|
return InstanceNorm2d(num_features=num_features, affine=affine)
|
|
|
|
|
|
class PixelNormFactory(NormalizationLayerFactory):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def create(self, num_features: int, affine: bool = True) -> Module:
|
|
return PixelNormalization()
|
|
|
|
|
|
class LayerNorm2d(Module):
|
|
def __init__(self, channels: int, affine: bool = True):
|
|
super(LayerNorm2d, self).__init__()
|
|
self.channels = channels
|
|
self.affine = affine
|
|
|
|
if self.affine:
|
|
self.weight = Parameter(torch.ones(1, channels, 1, 1))
|
|
self.bias = Parameter(torch.zeros(1, channels, 1, 1))
|
|
|
|
def forward(self, x):
|
|
shape = x.size()[1:]
|
|
y = layer_norm(x, shape) * self.weight + self.bias
|
|
return y
|
|
|
|
class LayerNorm2dFactory(NormalizationLayerFactory):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def create(self, num_features: int, affine: bool = True) -> Module:
|
|
return LayerNorm2d(channels=num_features, affine=affine)
|