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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 + 3.0, inplace=self.inplace) / 6.0 |
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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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return F.relu6(1.2 * x + 3.0, inplace=self.inplace) / 6.0 |
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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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