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89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
from jaxtyping import Float
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from torch import Tensor, device as Device, dtype as DType, nn, ones, sqrt, zeros
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from imaginairy.vendored.refiners.fluxion.layers.module import Module, WeightedModule
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class LayerNorm(nn.LayerNorm, WeightedModule):
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def __init__(
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self,
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normalized_shape: int | list[int],
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eps: float = 0.00001,
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device: Device | str | None = None,
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dtype: DType | None = None,
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) -> None:
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super().__init__( # type: ignore
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normalized_shape=normalized_shape,
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eps=eps,
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elementwise_affine=True, # otherwise not a WeightedModule
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device=device,
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dtype=dtype,
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)
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class GroupNorm(nn.GroupNorm, WeightedModule):
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def __init__(
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self,
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channels: int,
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num_groups: int,
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eps: float = 1e-5,
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device: Device | str | None = None,
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dtype: DType | None = None,
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) -> None:
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super().__init__( # type: ignore
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num_groups=num_groups,
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num_channels=channels,
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eps=eps,
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affine=True, # otherwise not a WeightedModule
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device=device,
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dtype=dtype,
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)
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self.channels = channels
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self.num_groups = num_groups
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self.eps = eps
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class LayerNorm2d(WeightedModule):
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"""
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2D Layer Normalization module.
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Parameters:
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channels (int): Number of channels in the input tensor.
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eps (float, optional): A small constant for numerical stability. Default: 1e-6.
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"""
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def __init__(
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self,
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channels: int,
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eps: float = 1e-6,
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device: Device | str | None = None,
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dtype: DType | None = None,
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) -> None:
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super().__init__()
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self.weight = nn.Parameter(ones(channels, device=device, dtype=dtype))
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self.bias = nn.Parameter(zeros(channels, device=device, dtype=dtype))
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self.eps = eps
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def forward(self, x: Float[Tensor, "batch channels height width"]) -> Float[Tensor, "batch channels height width"]:
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x_mean = x.mean(1, keepdim=True)
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x_var = (x - x_mean).pow(2).mean(1, keepdim=True)
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x_norm = (x - x_mean) / sqrt(x_var + self.eps)
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x_out = self.weight.unsqueeze(-1).unsqueeze(-1) * x_norm + self.bias.unsqueeze(-1).unsqueeze(-1)
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return x_out
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class InstanceNorm2d(nn.InstanceNorm2d, Module):
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def __init__(
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self,
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num_features: int,
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eps: float = 1e-05,
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device: Device | str | None = None,
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dtype: DType | None = None,
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) -> None:
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super().__init__( # type: ignore
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num_features=num_features,
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eps=eps,
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device=device,
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dtype=dtype,
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)
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