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main.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import random
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import copy
import numpy as np
from torchvision import datasets, transforms
import torch
import os
from utils.sampling import mnist_iid, mnist_noniid, cifar_iid,cifar_noniid
from utils.options import args_parser
from models.Update import LocalUpdate
from models.Nets import MLP, CNNMnist, CNNCifar, CNNFemnist, CharLSTM
from models.Fed import FedWeightAvg
from models.test import test_img
from utils.dataset import FEMNIST, ShakeSpeare
if __name__ == '__main__':
random.seed(123)
np.random.seed(123)
torch.manual_seed(123)
torch.cuda.manual_seed_all(123)
torch.cuda.manual_seed(123)
# parse args
args = args_parser()
args.device = torch.device('cuda:{}'.format(args.gpu) if torch.cuda.is_available() and args.gpu != -1 else 'cpu')
# load dataset and split users
if args.dataset == 'mnist':
trans_mnist = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
dataset_train = datasets.MNIST('./data/mnist/', train=True, download=True, transform=trans_mnist)
dataset_test = datasets.MNIST('./data/mnist/', train=False, download=True, transform=trans_mnist)
# sample users
if args.iid:
dict_users = mnist_iid(dataset_train, args.num_users)
else:
dict_users = mnist_noniid(dataset_train, args.num_users)
elif args.dataset == 'cifar':
#trans_cifar = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trans_cifar_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
trans_cifar_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
dataset_train = datasets.CIFAR10('./data/cifar', train=True, download=True, transform=trans_cifar_train)
dataset_test = datasets.CIFAR10('./data/cifar', train=False, download=True, transform=trans_cifar_test)
if args.iid:
dict_users = cifar_iid(dataset_train, args.num_users)
else:
dict_users = cifar_noniid(dataset_train, args.num_users)
elif args.dataset == 'fashion-mnist':
trans_fashion_mnist = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
dataset_train = datasets.FashionMNIST('./data/fashion-mnist', train=True, download=True,
transform=trans_fashion_mnist)
dataset_test = datasets.FashionMNIST('./data/fashion-mnist', train=False, download=True,
transform=trans_fashion_mnist)
if args.iid:
dict_users = mnist_iid(dataset_train, args.num_users)
else:
dict_users = mnist_noniid(dataset_train, args.num_users)
elif args.dataset == 'femnist':
dataset_train = FEMNIST(train=True)
dataset_test = FEMNIST(train=False)
dict_users = dataset_train.get_client_dic()
args.num_users = len(dict_users)
if args.iid:
exit('Error: femnist dataset is naturally non-iid')
else:
print("Warning: The femnist dataset is naturally non-iid, you do not need to specify iid or non-iid")
elif args.dataset == 'shakespeare':
dataset_train = ShakeSpeare(train=True)
dataset_test = ShakeSpeare(train=False)
dict_users = dataset_train.get_client_dic()
args.num_users = len(dict_users)
if args.iid:
exit('Error: ShakeSpeare dataset is naturally non-iid')
else:
print("Warning: The ShakeSpeare dataset is naturally non-iid, you do not need to specify iid or non-iid")
else:
exit('Error: unrecognized dataset')
img_size = dataset_train[0][0].shape
# build model
if args.model == 'cnn' and args.dataset == 'cifar':
net_glob = CNNCifar(args=args).to(args.device)
elif args.model == 'cnn' and (args.dataset == 'mnist' or args.dataset == 'fashion-mnist'):
net_glob = CNNMnist(args=args).to(args.device)
elif args.dataset == 'femnist' and args.model == 'cnn':
net_glob = CNNFemnist(args=args).to(args.device)
elif args.dataset == 'shakespeare' and args.model == 'lstm':
net_glob = CharLSTM().to(args.device)
elif args.model == 'mlp':
len_in = 1
for x in img_size:
len_in *= x
net_glob = MLP(dim_in=len_in, dim_hidden=64, dim_out=args.num_classes).to(args.device)
else:
exit('Error: unrecognized model')
print(net_glob)
net_glob.train()
# copy weights
w_glob = net_glob.state_dict()
# training
acc_test = []
clients = [LocalUpdate(args=args, dataset=dataset_train, idxs=dict_users[idx])
for idx in range(args.num_users)]
m, clients_index_array = max(int(args.frac * args.num_users), 1), range(args.num_users)
for iter in range(args.epochs):
w_locals, loss_locals, weight_locols= [], [], []
idxs_users = np.random.choice(clients_index_array, m, replace=False)
for idx in idxs_users:
w, loss = clients[idx].train(net=copy.deepcopy(net_glob).to(args.device))
w_locals.append(copy.deepcopy(w))
loss_locals.append(copy.deepcopy(loss))
weight_locols.append(len(dict_users[idx]))
# update global weights
w_glob = FedWeightAvg(w_locals, weight_locols)
# copy weight to net_glob
net_glob.load_state_dict(w_glob)
# print accuracy
net_glob.eval()
acc_t, loss_t = test_img(net_glob, dataset_test, args)
print("Round {:3d},Testing accuracy: {:.2f}".format(iter, acc_t))
acc_test.append(acc_t.item())
rootpath = './log'
if not os.path.exists(rootpath):
os.makedirs(rootpath)
accfile = open(rootpath + '/accfile_fed_{}_{}_{}_iid{}.dat'.
format(args.dataset, args.model, args.epochs, args.iid), "w")
for ac in acc_test:
sac = str(ac)
accfile.write(sac)
accfile.write('\n')
accfile.close()
# plot loss curve
plt.figure()
plt.plot(range(len(acc_test)), acc_test)
plt.ylabel('test accuracy')
plt.savefig(rootpath + '/fed_{}_{}_{}_C{}_iid{}_acc.png'.format(args.dataset, args.model, args.epochs, args.frac, args.iid))