FedCSS/models.py at main · Ace0001/FedCSS · GitHub

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import torch.nn as nn
import torch.nn.functional as functional
import torch.nn.init as init
import torch.nn.functional as F


def _weights_init(m):
    if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):
        init.kaiming_normal_(m.weight)


class LambdaLayer(nn.Module):
    def __init__(self, lambd):
        super(LambdaLayer, self).__init__()
        self.lambd = lambd

    def forward(self, x):
        return self.lambd(x)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, option='A'):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != planes:
            if option == 'A':
                """
                For CIFAR10 ResNet paper uses option A.
                """
                self.shortcut = LambdaLayer(lambda x:
                                            functional.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes//4, planes//4), "constant", 0))
            elif option == 'B':
                self.shortcut = nn.Sequential(
                    nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                    nn.BatchNorm2d(self.expansion * planes)
                )

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


class ResNet32(nn.Module):
    def __init__(self, args, num_classes, block=BasicBlock, num_blocks=[5, 5, 5]):
        super(ResNet32, self).__init__()
        a = 1
        if args.dataset_name == 'cifar10' or args.dataset_name == 'cifar100':
            a = 3
        elif args.dataset_name == 'mnist':
            a = 1
        self.in_planes = 16

        self.conv1 = nn.Conv2d(a, 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
        self.linear = nn.Linear(64, num_classes)

        self.apply(_weights_init)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion

        return nn.Sequential(*layers)

    def forward(self, x):
        out = functional.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = functional.avg_pool2d(out, out.size()[3])
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


class HiddenLayer(nn.Module):
    def __init__(self, input_size, output_size):
        super(HiddenLayer, self).__init__()
        self.fc = nn.Linear(input_size, output_size)
        self.relu = nn.ReLU()

    def forward(self, x):
        return self.relu(self.fc(x))


class VNet(nn.Module):
    def __init__(self, input, hidden1, output):
        super(VNet, self).__init__()
        self.linear1 = nn.Linear(input, hidden1)
        self.relu1 = nn.ReLU(inplace=True)
        self.linear2 = nn.Linear(hidden1, output)

    def forward(self, x):
        x = self.linear1(x)
        x = self.relu1(x)
        out = self.linear2(x)
        return F.sigmoid(out)


def load_VNet():
    model = VNet(1, 100, 1)
    return model