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main_fed.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import copy
import numpy as np
from torchvision import datasets, transforms
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from utils.sampling import mnist_iid, mnist_noniid, cifar_iid, covidx_iid
from utils.options import args_parser
from models.Update import LocalUpdate
from models.Nets import MLP, CNNMnist, CNNCifar, CNN, CovidNet
from models.Fed import FedAvg
from models.test import test_img
from data_loader.covidxdataset import COVIDxDataset
from models.metric import accuracy
from utils.util import print_stats, print_summary, select_model, select_optimizer, MetricTracker
import os
if __name__ == '__main__':
# 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))])
dataset_train = datasets.CIFAR10('../data/cifar', train=True, download=True, transform=trans_cifar)
dataset_test = datasets.CIFAR10('../data/cifar', train=False, download=True, transform=trans_cifar)
if args.iid:
dict_users = cifar_iid(dataset_train, args.num_users)
else:
exit('Error: only consider IID setting in CIFAR10')
elif args.dataset == 'covidx':
dataset_train = COVIDxDataset(mode='train', n_classes=args.num_classes, dataset_path=args.root_path,
dim=(224, 224))
dataset_test = COVIDxDataset(mode='test', n_classes=args.num_classes, dataset_path=args.root_path,
dim=(224, 224))
'''
train_params = {'batch_size': args.local_bs,
'shuffle': True,
'num_workers': 2}
test_params = {'batch_size': args.bs,
'shuffle': False,
'num_workers': 1}
train_generator = DataLoader(dataset_train, **train_params)
test_generator = DataLoader(dataset_test, **test_params)
'''
if args.iid:
dict_users = covidx_iid(dataset_train, args.num_users)
else:
exit('Error: only consider IID setting in COVIDx')
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':
net_glob = CNNMnist(args=args).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=200, dim_out=args.num_classes).to(args.device)
elif args.model == 'covidnet_small':
net_glob = CovidNet('small', n_classes=args.num_classes).to(args.device)
elif args.model == 'covidnet_large':
net_glob = CovidNet('large', n_classes=args.num_classes).to(args.device)
elif args.model in ['resnet18', 'mobilenet2', 'densenet169', 'resneXt']:
net_glob = CNN(args.num_classes, args.model).to(args.device)
else:
exit('Error: unrecognized model')
if args.recover != "none":
net_glob.load_state_dict(torch.load(args.recover))
print(net_glob)
net_glob.train()
# copy weights
w_glob = net_glob.state_dict()
# training
loss_train = []
cv_loss, cv_acc = [], []
val_loss_pre, counter = 0, 0
net_best = None
best_loss = None
val_acc_list, net_list = [], []
update_list = [0 for i in range(args.num_users)]
check_point = [i*10 for i in range(1, 10)]
print('v1.3')
for iter in range(args.start_ep, args.epochs):
w_locals, loss_locals = [], []
m = max(int(args.frac * args.num_users), 1)
idxs_users = np.random.choice(range(args.num_users), m, replace=False)
for idx in idxs_users:
local = LocalUpdate(args=args, dataset=dataset_train, idxs=dict_users[idx])
w, loss = local.train(net=copy.deepcopy(net_glob).to(args.device))
w_locals.append(copy.deepcopy(w))
loss_locals.append(copy.deepcopy(loss))
update_list[idx] += 1
print('updated user', idx)
# update global weights
w_glob = FedAvg(w_locals)
# copy weight to net_glob
net_glob.load_state_dict(w_glob)
if (iter+1) in check_point:
torch.save(net_glob.state_dict(), './save/fed_{}_{}_{}_C{}_iid{}_ckp{}.pkl'.format(args.dataset, args.model, args.epochs, args.frac, args.iid, iter+1))
# net_glob.eval()
# acc_train, loss_train = test_img(net_glob, dataset_train, args)
# acc_test, loss_test = test_img(net_glob, dataset_test, args)
# print("Training accuracy: {:.2f}".format(acc_train))
# print("Testing accuracy: {:.2f}".format(acc_test))
# net_glob.train()
# print loss
loss_avg = sum(loss_locals) / len(loss_locals)
print('Round {:3d}, Average loss {:.3f}'.format(iter, loss_avg))
loss_train.append(loss_avg)
# plot loss curve
np.save('./save/fed_{}_{}_{}_C{}_iid{}_loss.npy'.format(args.dataset, args.model, args.epochs, args.frac, args.iid), loss_train)
np.save('./save/fed_{}_{}_{}_C{}_iid{}_update.npy'.format(args.dataset, args.model, args.epochs, args.frac, args.iid), update_list)
torch.save(net_glob.state_dict(), './save/fed_{}_{}_{}_C{}_iid{}.pkl'.format(args.dataset, args.model, args.epochs, args.frac, args.iid))
plt.figure()
plt.plot(range(len(loss_train)), loss_train)
plt.ylabel('train_loss')
plt.savefig('./save/fed_{}_{}_{}_C{}_iid{}.png'.format(args.dataset, args.model, args.epochs, args.frac, args.iid))
# testing
net_glob.eval()
acc_train, loss_train = test_img(net_glob, dataset_train, args)
acc_test, loss_test = test_img(net_glob, dataset_test, args)
print("Training accuracy: {:.2f}".format(acc_train))
print("Testing accuracy: {:.2f}".format(acc_test))