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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. | |
# out = max(0, min(1, slop*x+offset)) | |
# paddle.fluid.layers.hard_sigmoid(x, slope=0.2, offset=0.5, name=None) | |
class Hsigmoid(nn.Module): | |
def __init__(self, inplace=True): | |
super(Hsigmoid, self).__init__() | |
self.inplace = inplace | |
def forward(self, x): | |
# torch: F.relu6(x + 3., inplace=self.inplace) / 6. | |
# paddle: F.relu6(1.2 * x + 3., inplace=self.inplace) / 6. | |
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) |