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ML examples: - credit_scoring: Credit risk prediction with HMEQ data - disease_prediction: Healthcare ML on encrypted records - cifar: Encrypted image classification - federated_learning: Distributed FHE training - hybrid_model: Mixed FHE/plaintext inference Deployment: - breast_cancer: Cancer detection API - sentiment_analysis: Encrypted NLP - server: FHE inference server Blockchain: - hardhat-template: Solidity + FHE - react-template: Full dApp with FHE
64 lines
2.6 KiB
Python
64 lines
2.6 KiB
Python
# MIT License
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#
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# Copyright (c) 2019 Xilinx
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# Original file can be found at https://github.com/Xilinx/brevitas/blob/8c3d9de0113528cf6693c6474a13d802a66682c6/src/brevitas_examples/bnn_pynq/models/tensor_norm.py
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import torch
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import torch.nn as nn
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import torch.nn.init as init
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class TensorNorm(nn.Module):
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def __init__(self, eps=1e-4, momentum=0.1):
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super().__init__()
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self.eps = eps
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self.momentum = momentum
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self.weight = nn.Parameter(torch.rand(1))
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self.bias = nn.Parameter(torch.rand(1))
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self.register_buffer("running_mean", torch.zeros(1))
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self.register_buffer("running_var", torch.ones(1))
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self.reset_running_stats()
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def reset_running_stats(self):
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self.running_mean.zero_()
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self.running_var.fill_(1)
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init.ones_(self.weight)
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init.zeros_(self.bias)
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def forward(self, x):
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if self.training:
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mean = x.mean()
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unbias_var = x.var(unbiased=True)
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biased_var = x.var(unbiased=False)
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self.running_mean = (
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1 - self.momentum
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) * self.running_mean + self.momentum * mean.detach()
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self.running_var = (
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1 - self.momentum
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) * self.running_var + self.momentum * unbias_var.detach()
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inv_std = 1 / (biased_var + self.eps).pow(0.5)
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return (x - mean) * inv_std * self.weight + self.bias
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else:
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return (
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(x - self.running_mean) / (self.running_var + self.eps).pow(0.5)
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) * self.weight + self.bias
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