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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)
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