# Copyright 2021 Ross Wightman . All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This code is modified by Zilliz.
from torch import nn
[docs]def hard_mish(x, inplace: bool = False):
if inplace:
return x.mul_(0.5 * (x + 2).clamp(min=0, max=2))
else:
return 0.5 * x * (x + 2).clamp(min=0, max=2)
[docs]class HardMish(nn.Module):
"""
Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
Args:
inplace(`Bool`):
whether use inplace version.
Returns:
(`torch.Tensor`)
output tensor after activation.
"""
[docs] def __init__(self, inplace: bool = False):
super().__init__()
self.inplace = inplace
[docs] def forward(self, x):
return hard_mish(x, self.inplace)
