| |
| import warnings |
|
|
| import torch |
| import torch.nn as nn |
| from mmengine.registry import MODELS |
|
|
|
|
| @MODELS.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 + 3) / 6, 0), 1) |
| |
| Note: |
| In MMCV v1.4.4, we modified the default value of args to align with |
| PyTorch official. |
| |
| Args: |
| bias (float): Bias of the input feature map. Default: 3.0. |
| divisor (float): Divisor of the input feature map. Default: 6.0. |
| 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: float = 3.0, |
| divisor: float = 6.0, |
| min_value: float = 0.0, |
| max_value: float = 1.0): |
| super().__init__() |
| warnings.warn( |
| 'In MMCV v1.4.4, we modified the default value of args to align ' |
| 'with PyTorch official. Previous Implementation: ' |
| 'Hsigmoid(x) = min(max((x + 1) / 2, 0), 1). ' |
| 'Current Implementation: ' |
| 'Hsigmoid(x) = min(max((x + 3) / 6, 0), 1).') |
| self.bias = bias |
| self.divisor = divisor |
| assert self.divisor != 0 |
| self.min_value = min_value |
| self.max_value = max_value |
|
|
| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| x = (x + self.bias) / self.divisor |
|
|
| return x.clamp_(self.min_value, self.max_value) |
|
|
