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# Copyright (c) OpenMMLab. All rights reserved.
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)
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