cifar100.py

# ===========================================================================
# Project:      On the Byzantine-Resilience of Distillation-Based Federated Learning - IOL Lab @ ZIB
# Paper:        arxiv.org/abs/2402.12265
# File:         models/cifar100.py
# Description:  CIFAR-100 Models
# ===========================================================================

import torch.nn.functional as F
from torch import nn


class WideResNet(nn.Module):
    # Based on https://github.com/meliketoy/wide-resnet.pytorch
    def __init__(self, depth=28, widen_factor=10, dropout_rate=0.3, num_classes=100):
        super(WideResNet, self).__init__()
        self.in_planes = 16

        assert ((depth - 4) % 6 == 0), 'Wide-resnet depth should be 6n+4'
        n = (depth - 4) / 6
        k = widen_factor

        nStages = [16, 16 * k, 32 * k, 64 * k]

        class wide_basic(nn.Module):
            def __init__(self, in_planes, planes, dropout_rate, stride=1):
                super(wide_basic, self).__init__()
                self.bn1 = nn.BatchNorm2d(in_planes)
                self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=True)

                self.dropout = nn.Dropout(p=dropout_rate)
                self.bn2 = nn.BatchNorm2d(planes)
                self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True)

                self.shortcut = nn.Sequential()
                if stride != 1 or in_planes != planes:
                    self.shortcut = nn.Sequential(
                        nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=True),
                    )

            def forward(self, x):
                out = self.dropout(self.conv1(F.relu(self.bn1(x))))
                out = self.conv2(F.relu(self.bn2(out)))
                out += self.shortcut(x)

                return out

        self.conv1 = self.conv3x3(3, nStages[0])
        self.layer1 = self._wide_layer(wide_basic, nStages[1], n, dropout_rate, stride=1)
        self.layer2 = self._wide_layer(wide_basic, nStages[2], n, dropout_rate, stride=2)
        self.layer3 = self._wide_layer(wide_basic, nStages[3], n, dropout_rate, stride=2)
        self.bn1 = nn.BatchNorm2d(nStages[3], momentum=0.9)
        self.linear = nn.Linear(nStages[3], num_classes)

    @staticmethod
    def conv3x3(in_planes, out_planes, stride=1):
        return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)

    def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
        strides = [stride] + [1] * (int(num_blocks) - 1)
        layers = []

        for stride in strides:
            layers.append(block(self.in_planes, planes, dropout_rate, stride))
            self.in_planes = planes

        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = F.relu(self.bn1(out))
        out = F.avg_pool2d(out, 8)
        out = out.view(out.size(0), -1)
        out = self.linear(out)

        return out