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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
class Scale(nn.Module):
"""A learnable scale parameter.
This layer scales the input by a learnable factor. It multiplies a
learnable scale parameter of shape (1,) with input of any shape.
Args:
scale (float): Initial value of scale factor. Default: 1.0
"""
def __init__(self, scale: float = 1.0):
super().__init__()
self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x * self.scale
class LayerScale(nn.Module):
"""LayerScale layer.
Args:
dim (int): Dimension of input features.
inplace (bool): Whether performs operation in-place.
Default: `False`.
data_format (str): The input data format, could be 'channels_last'
or 'channels_first', representing (B, C, H, W) and
(B, N, C) format data respectively. Default: 'channels_last'.
scale (float): Initial value of scale factor. Default: 1.0
"""
def __init__(self,
dim: int,
inplace: bool = False,
data_format: str = 'channels_last',
scale: float = 1e-5):
super().__init__()
assert data_format in ('channels_last', 'channels_first'), \
"'data_format' could only be channels_last or channels_first."
self.inplace = inplace
self.data_format = data_format
self.weight = nn.Parameter(torch.ones(dim) * scale)
def forward(self, x) -> torch.Tensor:
if self.data_format == 'channels_first':
shape = tuple((1, -1, *(1 for _ in range(x.dim() - 2))))
else:
shape = tuple((*(1 for _ in range(x.dim() - 1)), -1))
if self.inplace:
return x.mul_(self.weight.view(*shape))
else:
return x * self.weight.view(*shape)
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