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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
142 lines
5.4 KiB
Python
142 lines
5.4 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/CNV.py
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import torch
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from brevitas.core.restrict_val import RestrictValueType
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from brevitas.nn import QuantConv2d, QuantIdentity, QuantLinear
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from torch.nn import AvgPool2d, BatchNorm1d, BatchNorm2d, Module, ModuleList
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from .common import CommonActQuant, CommonWeightQuant
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from .tensor_norm import TensorNorm
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CNV_OUT_CH_POOL = [(64, False), (64, True), (128, False), (128, True), (256, False), (256, False)]
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INTERMEDIATE_FC_FEATURES = [(256, 512), (512, 512)]
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LAST_FC_IN_FEATURES = 512
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LAST_FC_PER_OUT_CH_SCALING = False
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POOL_SIZE = 2
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KERNEL_SIZE = 3
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class CNV(Module):
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def __init__(self, num_classes, weight_bit_width, act_bit_width, in_bit_width, in_ch):
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super(CNV, self).__init__()
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self.conv_features = ModuleList()
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self.linear_features = ModuleList()
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self.conv_features.append(
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QuantIdentity( # for Q1.7 input format
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act_quant=CommonActQuant,
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return_quant_tensor=True,
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bit_width=in_bit_width,
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min_val=-1.0,
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max_val=1.0 - 2.0 ** (-7),
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narrow_range=False,
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restrict_scaling_type=RestrictValueType.POWER_OF_TWO,
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)
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)
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for out_ch, is_pool_enabled in CNV_OUT_CH_POOL:
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self.conv_features.append(
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QuantConv2d(
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kernel_size=KERNEL_SIZE,
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in_channels=in_ch,
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out_channels=out_ch,
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bias=False,
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weight_quant=CommonWeightQuant,
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weight_bit_width=weight_bit_width,
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)
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)
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in_ch = out_ch
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self.conv_features.append(BatchNorm2d(in_ch, eps=1e-4))
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self.conv_features.append(
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QuantIdentity(act_quant=CommonActQuant, bit_width=act_bit_width)
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)
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if is_pool_enabled:
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self.conv_features.append(AvgPool2d(kernel_size=2))
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self.conv_features.append(
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QuantIdentity(act_quant=CommonActQuant, bit_width=act_bit_width)
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)
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for in_features, out_features in INTERMEDIATE_FC_FEATURES:
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self.linear_features.append(
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QuantLinear(
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in_features=in_features,
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out_features=out_features,
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bias=False,
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weight_quant=CommonWeightQuant,
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weight_bit_width=weight_bit_width,
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)
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)
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self.linear_features.append(BatchNorm1d(out_features, eps=1e-4))
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self.linear_features.append(
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QuantIdentity(act_quant=CommonActQuant, bit_width=act_bit_width)
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)
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self.linear_features.append(
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QuantLinear(
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in_features=LAST_FC_IN_FEATURES,
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out_features=num_classes,
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bias=False,
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weight_quant=CommonWeightQuant,
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weight_bit_width=weight_bit_width,
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)
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)
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self.linear_features.append(TensorNorm())
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for m in self.modules():
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if isinstance(m, QuantConv2d) or isinstance(m, QuantLinear):
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torch.nn.init.uniform_(m.weight.data, -1, 1)
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def clip_weights(self, min_val, max_val):
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for mod in self.conv_features:
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if isinstance(mod, QuantConv2d):
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mod.weight.data.clamp_(min_val, max_val)
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for mod in self.linear_features:
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if isinstance(mod, QuantLinear):
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mod.weight.data.clamp_(min_val, max_val)
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def forward(self, x):
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for mod in self.conv_features:
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x = mod(x)
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x = torch.flatten(x, 1)
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for mod in self.linear_features:
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x = mod(x)
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return x
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def cnv(cfg):
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weight_bit_width = cfg.getint("QUANT", "WEIGHT_BIT_WIDTH")
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act_bit_width = cfg.getint("QUANT", "ACT_BIT_WIDTH")
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in_bit_width = cfg.getint("QUANT", "IN_BIT_WIDTH")
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num_classes = cfg.getint("MODEL", "NUM_CLASSES")
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in_channels = cfg.getint("MODEL", "IN_CHANNELS")
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net = CNV(
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weight_bit_width=weight_bit_width,
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act_bit_width=act_bit_width,
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in_bit_width=in_bit_width,
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num_classes=num_classes,
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in_ch=in_channels,
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)
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return net
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