74 lines
2.1 KiB
Python
74 lines
2.1 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class Hswish(nn.Module):
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def __init__(self, inplace=True):
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super(Hswish, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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return x * F.relu6(x + 3., inplace=self.inplace) / 6.
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# out = max(0, min(1, slop*x+offset))
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# paddle.fluid.layers.hard_sigmoid(x, slope=0.2, offset=0.5, name=None)
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class Hsigmoid(nn.Module):
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def __init__(self, inplace=True):
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super(Hsigmoid, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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# torch: F.relu6(x + 3., inplace=self.inplace) / 6.
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# paddle: F.relu6(1.2 * x + 3., inplace=self.inplace) / 6.
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return F.relu6(1.2 * x + 3., inplace=self.inplace) / 6.
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class GELU(nn.Module):
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def __init__(self, inplace=True):
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super(GELU, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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return torch.nn.functional.gelu(x)
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class Swish(nn.Module):
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def __init__(self, inplace=True):
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super(Swish, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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if self.inplace:
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x.mul_(torch.sigmoid(x))
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return x
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else:
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return x*torch.sigmoid(x)
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class Activation(nn.Module):
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def __init__(self, act_type, inplace=True):
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super(Activation, self).__init__()
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act_type = act_type.lower()
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if act_type == 'relu':
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self.act = nn.ReLU(inplace=inplace)
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elif act_type == 'relu6':
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self.act = nn.ReLU6(inplace=inplace)
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elif act_type == 'sigmoid':
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raise NotImplementedError
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elif act_type == 'hard_sigmoid':
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self.act = Hsigmoid(inplace)
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elif act_type == 'hard_swish':
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self.act = Hswish(inplace=inplace)
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elif act_type == 'leakyrelu':
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self.act = nn.LeakyReLU(inplace=inplace)
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elif act_type == 'gelu':
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self.act = GELU(inplace=inplace)
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elif act_type == 'swish':
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self.act = Swish(inplace=inplace)
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else:
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raise NotImplementedError
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def forward(self, inputs):
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return self.act(inputs)
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