| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354 |
- import torch
- from torch import nn
- class LayerScale(nn.Module):
- """ LayerScale on tensors with channels in last-dim.
- """
- def __init__(
- self,
- dim: int,
- init_values: float = 1e-5,
- inplace: bool = False,
- device=None,
- dtype=None,
- ) -> None:
- super().__init__()
- self.init_values = init_values
- self.inplace = inplace
- self.gamma = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
- self.reset_parameters()
- def reset_parameters(self):
- torch.nn.init.constant_(self.gamma, self.init_values)
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- return x.mul_(self.gamma) if self.inplace else x * self.gamma
- class LayerScale2d(nn.Module):
- """ LayerScale for tensors with torch 2D NCHW layout.
- """
- def __init__(
- self,
- dim: int,
- init_values: float = 1e-5,
- inplace: bool = False,
- device=None,
- dtype=None,
- ):
- super().__init__()
- self.init_values = init_values
- self.inplace = inplace
- self.gamma = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
- self.reset_parameters()
- def reset_parameters(self):
- torch.nn.init.constant_(self.gamma, self.init_values)
- def forward(self, x):
- gamma = self.gamma.view(1, -1, 1, 1)
- return x.mul_(gamma) if self.inplace else x * gamma
|
