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Zero
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
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)
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