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7965430 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | # Copyright (c) 2022, NVIDIA CORPORATION. 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.
###############################################################################
import torch
class GaussianDropout(torch.nn.Module):
"""
Gaussian dropout using multiplicative gaussian noise.
https://keras.io/api/layers/regularization_layers/gaussian_dropout/
Can be an effective alternative bottleneck to VAE or VQ:
https://www.deepmind.com/publications/gaussian-dropout-as-an-information-bottleneck-layer
Unlike some other implementations, this takes the standard deviation of the noise as input
instead of the 'rate' typically defined as: stdev = sqrt(rate / (1 - rate))
"""
def __init__(self, stdev=1.0):
super(GaussianDropout, self).__init__()
self.stdev = stdev
def forward(self, inputs):
if not self.training:
return inputs
noise = torch.normal(mean=1.0, std=self.stdev, size=inputs.shape, device=inputs.device)
out = noise * inputs
return out
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