# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import torch
from torch import nn
from monai.utils import optional_import
if optional_import("torch.nn.functional", name="mish")[1]:
def monai_mish(x, inplace: bool = False):
return torch.nn.functional.mish(x, inplace=inplace)
else:
def monai_mish(x, inplace: bool = False):
return x * torch.tanh(torch.nn.functional.softplus(x))
if optional_import("torch.nn.functional", name="silu")[1]:
def monai_swish(x, inplace: bool = False):
return torch.nn.functional.silu(x, inplace=inplace)
else:
def monai_swish(x, inplace: bool = False):
return SwishImplementation.apply(x)
[docs]
class Swish(nn.Module):
r"""Applies the element-wise function:
.. math::
\text{Swish}(x) = x * \text{Sigmoid}(\alpha * x) ~~~~\text{for constant value}~ \alpha.
Citation: Searching for Activation Functions, Ramachandran et al., 2017, https://arxiv.org/abs/1710.05941.
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> import torch
>>> from monai.networks.layers.factories import Act
>>> m = Act['swish']()
>>> input = torch.randn(2)
>>> output = m(input)
"""
def __init__(self, alpha=1.0):
super().__init__()
self.alpha = alpha
[docs]
def forward(self, input: torch.Tensor) -> torch.Tensor:
return input * torch.sigmoid(self.alpha * input)
class SwishImplementation(torch.autograd.Function):
r"""Memory efficient implementation for training
Follows recommendation from:
https://github.com/lukemelas/EfficientNet-PyTorch/issues/18#issuecomment-511677853
Results in ~ 30% memory saving during training as compared to Swish()
"""
@staticmethod
def forward(ctx, input):
result = input * torch.sigmoid(input)
ctx.save_for_backward(input)
return result
@staticmethod
def backward(ctx, grad_output):
input = ctx.saved_tensors[0]
sigmoid_input = torch.sigmoid(input)
return grad_output * (sigmoid_input * (1 + input * (1 - sigmoid_input)))
[docs]
class MemoryEfficientSwish(nn.Module):
r"""Applies the element-wise function:
.. math::
\text{Swish}(x) = x * \text{Sigmoid}(\alpha * x) ~~~~\text{for constant value}~ \alpha=1.
Memory efficient implementation for training following recommendation from:
https://github.com/lukemelas/EfficientNet-PyTorch/issues/18#issuecomment-511677853
Results in ~ 30% memory saving during training as compared to Swish()
Citation: Searching for Activation Functions, Ramachandran et al., 2017, https://arxiv.org/abs/1710.05941.
From Pytorch 1.7.0+, the optimized version of `Swish` named `SiLU` is implemented,
this class will utilize `torch.nn.functional.silu` to do the calculation if meets the version.
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> import torch
>>> from monai.networks.layers.factories import Act
>>> m = Act['memswish']()
>>> input = torch.randn(2)
>>> output = m(input)
"""
def __init__(self, inplace: bool = False):
super().__init__()
# inplace only works when using torch.nn.functional.silu
self.inplace = inplace
[docs]
def forward(self, input: torch.Tensor):
return monai_swish(input, self.inplace)
[docs]
class Mish(nn.Module):
r"""Applies the element-wise function:
.. math::
\text{Mish}(x) = x * tanh(\text{softplus}(x)).
Citation: Mish: A Self Regularized Non-Monotonic Activation Function, Diganta Misra, 2019, https://arxiv.org/abs/1908.08681.
From Pytorch 1.9.0+, the optimized version of `Mish` is implemented,
this class will utilize `torch.nn.functional.mish` to do the calculation if meets the version.
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> import torch
>>> from monai.networks.layers.factories import Act
>>> m = Act['mish']()
>>> input = torch.randn(2)
>>> output = m(input)
"""
def __init__(self, inplace: bool = False):
super().__init__()
# inplace only works when using torch.nn.functional.mish
self.inplace = inplace
[docs]
def forward(self, input: torch.Tensor):
return monai_mish(input, self.inplace)
[docs]
class GEGLU(nn.Module):
r"""Applies the element-wise function:
.. math::
\text{GEGLU}(x) = x_1 * \text{Sigmoid}(x_2)
where :math:`x_1` and :math:`x_2` are split from the input tensor along the last dimension.
Citation: GLU Variants Improve Transformer, Noam Shazeer, 2020, https://arxiv.org/abs/2002.05202.
Shape:
- Input: :math:`(N, *, 2 * D)`
- Output: :math:`(N, *, D)`, where `*` means, any number of additional dimensions
"""
[docs]
def forward(self, input: torch.Tensor):
x, gate = input.chunk(2, dim=-1)
return x * nn.functional.gelu(gate)
