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Running
on
Zero
# ------------------------------------------------------------------------ | |
# Copyright (c) 2023-present, BAAI. 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. | |
# ------------------------------------------------------------------------ | |
# pyre-unsafe | |
"""Drop regularization layers.""" | |
from torch import nn | |
class DropPathV(nn.Module): | |
"""Set examples to zero randomly.""" | |
def __init__(self, p=0.1, inplace=False): | |
super(DropPathV, self).__init__() | |
self.p = p | |
self.inplace = inplace | |
def forward(self, input): | |
if not self.training or self.p <= 0: | |
return input | |
keep_p = 1 - self.p | |
shape = (input.shape[0],) + (1,) * (input.dim() - 1) | |
scale = input.new_empty(shape).bernoulli_(keep_p).div_(keep_p) | |
return input.mul_(scale) if self.inplace else input.mul(scale) | |
def extra_repr(self): | |
inplace_str = ", inplace" if self.inplace else "" | |
return "p={}{}".format(self.p, inplace_str) | |