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