|
|
|
|
|
import torch |
|
import torch.nn as nn |
|
import torch.nn.functional as F |
|
|
|
|
|
class Hswish(nn.Module): |
|
def __init__(self, inplace=True): |
|
super(Hswish, self).__init__() |
|
self.inplace = inplace |
|
|
|
def forward(self, x): |
|
return x * F.relu6(x + 3., inplace=self.inplace) / 6. |
|
|
|
|
|
|
|
class Hsigmoid(nn.Module): |
|
def __init__(self, inplace=True): |
|
super(Hsigmoid, self).__init__() |
|
self.inplace = inplace |
|
|
|
def forward(self, x): |
|
|
|
|
|
return F.relu6(1.2 * x + 3., inplace=self.inplace) / 6. |
|
|
|
class GELU(nn.Module): |
|
def __init__(self, inplace=True): |
|
super(GELU, self).__init__() |
|
self.inplace = inplace |
|
|
|
def forward(self, x): |
|
return torch.nn.functional.gelu(x) |
|
|
|
|
|
class Swish(nn.Module): |
|
def __init__(self, inplace=True): |
|
super(Swish, self).__init__() |
|
self.inplace = inplace |
|
|
|
def forward(self, x): |
|
if self.inplace: |
|
x.mul_(torch.sigmoid(x)) |
|
return x |
|
else: |
|
return x*torch.sigmoid(x) |
|
|
|
|
|
class Activation(nn.Module): |
|
def __init__(self, act_type, inplace=True): |
|
super(Activation, self).__init__() |
|
act_type = act_type.lower() |
|
if act_type == 'relu': |
|
self.act = nn.ReLU(inplace=inplace) |
|
elif act_type == 'relu6': |
|
self.act = nn.ReLU6(inplace=inplace) |
|
elif act_type == 'sigmoid': |
|
raise NotImplementedError |
|
elif act_type == 'hard_sigmoid': |
|
self.act = Hsigmoid(inplace) |
|
elif act_type == 'hard_swish': |
|
self.act = Hswish(inplace=inplace) |
|
elif act_type == 'leakyrelu': |
|
self.act = nn.LeakyReLU(inplace=inplace) |
|
elif act_type == 'gelu': |
|
self.act = GELU(inplace=inplace) |
|
elif act_type == 'swish': |
|
self.act = Swish(inplace=inplace) |
|
else: |
|
raise NotImplementedError |
|
|
|
def forward(self, inputs): |
|
return self.act(inputs) |