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import torch.nn as nn |
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from .registry import ACTIVATION_LAYERS |
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@ACTIVATION_LAYERS.register_module() |
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class HSigmoid(nn.Module): |
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"""Hard Sigmoid Module. Apply the hard sigmoid function: |
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Hsigmoid(x) = min(max((x + bias) / divisor, min_value), max_value) |
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Default: Hsigmoid(x) = min(max((x + 1) / 2, 0), 1) |
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Args: |
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bias (float): Bias of the input feature map. Default: 1.0. |
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divisor (float): Divisor of the input feature map. Default: 2.0. |
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min_value (float): Lower bound value. Default: 0.0. |
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max_value (float): Upper bound value. Default: 1.0. |
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Returns: |
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Tensor: The output tensor. |
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""" |
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def __init__(self, bias=1.0, divisor=2.0, min_value=0.0, max_value=1.0): |
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super(HSigmoid, self).__init__() |
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self.bias = bias |
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self.divisor = divisor |
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assert self.divisor != 0 |
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self.min_value = min_value |
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self.max_value = max_value |
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def forward(self, x): |
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x = (x + self.bias) / self.divisor |
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return x.clamp_(self.min_value, self.max_value) |
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