FedNets.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6

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

torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.cuda.manual_seed(1)

class MLP(nn.Module):
    def __init__(self, dim_in, dim_hidden, dim_out):
        super(MLP, self).__init__()
        print("NN: MLP is created")
        self.layer_input = nn.Linear(dim_in, dim_hidden)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout()
        self.layer_hidden = nn.Linear(dim_hidden, dim_out)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        x = x.view(-1, x.shape[1]*x.shape[-2]*x.shape[-1])
        x = self.layer_input(x)
        x = self.dropout(x)
        x = self.relu(x)
        x = self.layer_hidden(x)
        return self.softmax(x)

class MLP1(nn.Module):
    def __init__(self, dim_in, dim_hidden, dim_out):
        super(MLP1, self).__init__()
        print("NN: MLP is created")
        self.layer_input = nn.Linear(dim_in, dim_hidden)
        self.layer_hidden = nn.Linear(dim_hidden, dim_out)
        # self.softmax = nn.Softmax(dim=1)

        # weights_init = 0.001
        # bias_init = 0.001
        #
        # nn.init.constant_(self.layer_input.weight,weights_init)
        # nn.init.constant_(self.layer_input.bias, bias_init)
        # nn.init.constant_(self.layer_hidden.weight, weights_init)
        # nn.init.constant_(self.layer_hidden.bias, bias_init)

    def forward(self, x):
        x = x.view(-1, x.shape[1]*x.shape[-2]*x.shape[-1])
        # x = x.view(-1, 1)
        x = self.layer_input(x)
        x = self.layer_hidden(x)
        return F.log_softmax(x, dim=1)

class MLP_regression(nn.Module):
    def __init__(self, dim_in, dim_hidden, dim_out):
        super(MLP_regression, self).__init__()
        print("NN: MLP is created")
        self.layer_input = nn.Linear(dim_in, dim_hidden)
        self.layer_hidden = nn.Linear(dim_hidden, dim_out)

    def forward(self, x):
        x = x.view(-1, x.shape[1]*x.shape[-2]*x.shape[-1])
        x = self.layer_input(x)
        x = self.layer_hidden(x)
        return F.log_softmax(x, dim=1)

class CNN_test(nn.Module):
    def __init__(self, args):
        super(CNN_test, self).__init__()
        self.conv1 = nn.Sequential(         # input shape (1, 28, 28)
            nn.Conv2d(
                in_channels=1,              # input height
                out_channels=16,            # n_filters
                kernel_size=5,              # filter size
                stride=1,                   # filter movement/step
                padding=2,                  # if want same width and length of this image after Conv2d, padding=(kernel_size-1)/2 if stride=1
            ),                              # output shape (16, 28, 28)
            nn.ReLU(),                      # activation
            nn.MaxPool2d(kernel_size=2),    # choose max value in 2x2 area, output shape (16, 14, 14)
        )
        self.conv2 = nn.Sequential(         # input shape (16, 14, 14)
            nn.Conv2d(16, 32, 5, 1, 2),     # output shape (32, 14, 14)
            nn.ReLU(),                      # activation
            nn.MaxPool2d(2),                # output shape (32, 7, 7)
        )
        self.out = nn.Linear(32* 7 * 7, args.num_classes)   # fully connected layer, output 10 classes

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0), -1)           # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
        output = self.out(x)
        return F.log_softmax(output, dim=1)                    # return x for visualization


class CNNMnist(nn.Module):
    def __init__(self, args):
        super(CNNMnist, self).__init__()
        print("NN: CNNMnist is created")
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, args.num_classes)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, x.shape[1]*x.shape[2]*x.shape[3])
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


class CNNCifar(nn.Module):
    def __init__(self, args):
        super(CNNCifar, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, args.num_classes)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return F.log_softmax(x, dim=1)