/
main_lg.py
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/
main_lg.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import copy
import os
import pickle
import itertools
import pandas as pd
import numpy as np
import torch
from utils.options import args_parser
from utils.train_utils import get_data, get_model
from models.Update import LocalUpdate
from models.test import test_img_local_all, test_img_avg_all, test_img_ensemble_all, test_img_local
import pdb
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')
base_dir = './save/{}/{}_iid{}_num{}_C{}_le{}/shard{}/{}/'.format(
args.dataset, args.model, args.iid, args.num_users, args.frac, args.local_ep, args.shard_per_user, args.results_save)
assert(len(args.load_fed) > 0)
base_save_dir = os.path.join(base_dir, 'lg/{}'.format(args.load_fed))
if not os.path.exists(base_save_dir):
os.makedirs(base_save_dir, exist_ok=True)
dataset_train, dataset_test, dict_users_train, dict_users_test = get_data(args)
dict_save_path = os.path.join(base_dir, 'dict_users.pkl')
with open(dict_save_path, 'rb') as handle:
dict_users_train, dict_users_test = pickle.load(handle)
# build model
net_glob = get_model(args)
net_glob.train()
fed_model_path = os.path.join(base_dir, 'fed/{}'.format(args.load_fed))
net_glob.load_state_dict(torch.load(fed_model_path))
total_num_layers = len(net_glob.weight_keys)
w_glob_keys = net_glob.weight_keys[total_num_layers - args.num_layers_keep:]
w_glob_keys = list(itertools.chain.from_iterable(w_glob_keys))
num_param_glob = 0
num_param_local = 0
for key in net_glob.state_dict().keys():
num_param_local += net_glob.state_dict()[key].numel()
if key in w_glob_keys:
num_param_glob += net_glob.state_dict()[key].numel()
percentage_param = 100 * float(num_param_glob) / num_param_local
print('# Params: {} (local), {} (global); Percentage {:.2f} ({}/{})'.format(
num_param_local, num_param_glob, percentage_param, num_param_glob, num_param_local))
# generate list of local models for each user
net_local_list = []
for user in range(args.num_users):
net_local_list.append(copy.deepcopy(net_glob))
acc_test_avg, loss_test_avg = test_img_avg_all(net_glob, net_local_list, args, dataset_test)
acc_test_local_list, _ = test_img_local_all(net_local_list, args, dataset_test, dict_users_test, return_all=True)
acc_test_local = acc_test_local_list.mean()
# training
results_save_path = os.path.join(base_save_dir, 'results.csv')
results_columns = ['epoch', 'acc_test_local', 'acc_test_avg', 'best_acc_local', 'acc_test_ens_avg', 'acc_test_ens_maj']
loss_train = []
best_iter = -1
best_acc_local = -1
best_acc_list = acc_test_local_list
best_net_list = copy.deepcopy(net_local_list)
fina_net_list = copy.deepcopy(net_local_list)
results = []
results.append(np.array([-1, acc_test_local, acc_test_avg, acc_test_local, None, None]))
print('Round {:3d}, Acc (local): {:.2f}, Acc (avg): {:.2f}, Acc (local-best): {:.2f}'.format(
-1, acc_test_local, acc_test_avg, acc_test_local))
for iter in range(args.epochs):
w_glob = {}
loss_locals = []
m = max(int(args.frac * args.num_users), 1)
idxs_users = np.random.choice(range(args.num_users), m, replace=False)
w_keys_epoch = w_glob_keys
for idx in idxs_users:
local = LocalUpdate(args=args, dataset=dataset_train, idxs=dict_users_train[idx])
net_local = net_local_list[idx]
w_local, loss = local.train(net=net_local.to(args.device), lr=args.lr)
loss_locals.append(copy.deepcopy(loss))
# sum up weights
if len(w_glob) == 0:
w_glob = copy.deepcopy(w_local)
else:
for k in w_keys_epoch:
w_glob[k] += w_local[k]
loss_avg = sum(loss_locals) / len(loss_locals)
loss_train.append(loss_avg)
# get weighted average for global weights
for k in w_keys_epoch:
w_glob[k] = torch.div(w_glob[k], m)
# copy weights to each local model
for idx in range(args.num_users):
net_local = net_local_list[idx]
w_local = net_local.state_dict()
for k in w_keys_epoch:
w_local[k] = w_glob[k]
net_local.load_state_dict(w_local)
# find best local models from after round
acc_test_local_list, _ = test_img_local_all(net_local_list, args, dataset_test, dict_users_test, return_all=True)
for user in range(args.num_users):
if acc_test_local_list[user] >= best_acc_list[user]:
best_acc_list[user] = acc_test_local_list[user]
best_net_list[user] = copy.deepcopy(net_local_list[user])
# average best models for local test
acc_test_avg, loss_test_avg, net_glob = test_img_avg_all(net_glob, best_net_list, args, dataset_test, return_net=True)
# average global layers of the best local models
best_net_list_avg = copy.deepcopy(best_net_list)
w_glob = net_glob.state_dict()
for net_local in best_net_list_avg:
w_local = net_local.state_dict()
for k in w_keys_epoch:
w_local[k] = w_glob[k]
net_local.load_state_dict(w_local)
acc_test_local, _ = test_img_local_all(best_net_list_avg, args, dataset_test, dict_users_test)
if acc_test_local > best_acc_local:
best_acc_local = acc_test_local
best_iter = iter
final_net_list = copy.deepcopy(best_net_list_avg)
print('Round {:3d}, Acc (local): {:.2f}, Acc (avg): {:.2f}, Acc (local-best): {:.2f}'.format(
iter, acc_test_local, acc_test_avg, best_acc_local))
results.append(np.array([iter, acc_test_local, acc_test_avg, best_acc_local, None, None]))
final_results = np.array(results)
final_results = pd.DataFrame(final_results, columns=results_columns)
final_results.to_csv(results_save_path, index=False)
acc_test_local, loss_test_local = test_img_local_all(final_net_list, args, dataset_test, dict_users_test)
acc_test_avg, loss_test_avg = test_img_avg_all(net_glob, final_net_list, args, dataset_test)
acc_test_ens_avg, loss_test, acc_test_ens_maj = test_img_ensemble_all(final_net_list, args, dataset_test)
print('Best model, acc (local): {}, acc (avg): {}, acc (ens,avg): {}, acc (ens,maj): {}'.format(
acc_test_local, acc_test_avg, acc_test_ens_avg, acc_test_ens_maj))
results.append(np.array([best_iter, None, acc_test_avg, acc_test_local, acc_test_ens_avg, acc_test_ens_maj]))
final_results = np.array(results)
final_results = pd.DataFrame(final_results, columns=results_columns)
final_results.to_csv(results_save_path, index=False)
# save models
for user, net_local in enumerate(final_net_list):
model_save_path = os.path.join(base_save_dir, 'model_user{}.pt'.format(user))
torch.save(net_local.state_dict(), model_save_path)