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