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45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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###############################################################################
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import torch
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class GaussianDropout(torch.nn.Module):
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"""
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Gaussian dropout using multiplicative gaussian noise.
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https://keras.io/api/layers/regularization_layers/gaussian_dropout/
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Can be an effective alternative bottleneck to VAE or VQ:
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https://www.deepmind.com/publications/gaussian-dropout-as-an-information-bottleneck-layer
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Unlike some other implementations, this takes the standard deviation of the noise as input
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instead of the 'rate' typically defined as: stdev = sqrt(rate / (1 - rate))
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"""
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def __init__(self, stdev=1.0):
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super(GaussianDropout, self).__init__()
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self.stdev = stdev
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def forward(self, inputs):
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if not self.training:
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return inputs
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noise = torch.normal(mean=1.0, std=self.stdev, size=inputs.shape, device=inputs.device)
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out = noise * inputs
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return out
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