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main_fedrep.py
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main_fedrep.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import copy
import pickle
import numpy as np
import pandas as pd
import torch
from utils.options import args_parser
from utils.train_utils import get_data, get_model
from models.Update import LocalUpdateFedRep
from models.test import test_img, test_img_local, test_img_local_all
from models.Fed import FedAvg
import os
import pdb
if __name__ == '__main__':
# parse args
args = args_parser()
# set seed
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
np.random.seed(args.seed)
args.device = torch.device('cuda:{}'.format(args.gpu) if torch.cuda.is_available() and args.gpu != -1 else 'cpu')
if args.unbalanced:
base_dir = './save/{}/{}_iid{}_num{}_C{}_le{}/shard{}_unbalanced_bu{}_md{}/{}/'.format(
args.dataset, args.model, args.iid, args.num_users, args.frac, args.local_ep, args.shard_per_user, args.num_batch_users, args.moved_data_size, args.results_save)
else:
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)
algo_dir = "fedrep"
if not os.path.exists(os.path.join(base_dir, algo_dir)):
os.makedirs(os.path.join(base_dir, algo_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, algo_dir, 'dict_users.pkl')
with open(dict_save_path, 'wb') as handle:
pickle.dump((dict_users_train, dict_users_test), handle)
# build a global model
net_glob = get_model(args)
net_glob.train()
# build local models
net_local_list = []
for user_idx in range(args.num_users):
net_local_list.append(copy.deepcopy(net_glob))
# training
results_save_path = os.path.join(base_dir, algo_dir, 'results.csv')
loss_train = []
net_best = None
best_loss = None
best_acc = None
best_epoch = None
lr = args.lr
results = []
w_glob = None
update_keys = [k for k in net_glob.state_dict().keys() if 'linear' not in k]
print("all keys", net_glob.state_dict().keys())
print("aggregation keys", update_keys)
w_glob = {k: net_glob.state_dict()[k] for k in update_keys}
for iter in range(args.epochs):
loss_locals = []
m = max(int(args.frac * args.num_users), 1)
idxs_users = np.random.choice(range(args.num_users), m, replace=False)
w_locals = []
# local updates
for idx in idxs_users:
net_local = copy.deepcopy(net_local_list[idx])
# Server sends current representation φ^t to these clients
net_local.load_state_dict(w_glob, strict=False)
local = LocalUpdateFedRep(args=args, dataset=dataset_train, idxs=dict_users_train[idx])
w = local.train(net=copy.deepcopy(net_local).to(args.device), lr=lr)
w_locals.append(copy.deepcopy(w))
net_local_list[idx].load_state_dict(copy.deepcopy(w))
if (iter + 1) in [args.epochs//2, (args.epochs*3)//4]:
lr *= 0.1
# update global weights
w_glob = FedAvg(w_locals)
w_glob = {k: w_glob[k] for k in update_keys}
net_glob.load_state_dict(w_glob, strict=False)
# - Evaluation - #
# Backup local parameters
backup_net_local_list = copy.deepcopy(net_local_list)
backup_local_epoch = args.local_ep
# fine-tuning
for idx in range(args.num_users):
net_local = copy.deepcopy(net_local_list[idx])
# Server sends current representation φ^t to these clients
net_local.load_state_dict(w_glob, strict=False)
local = LocalUpdateFedRep(args=args, dataset=dataset_train, idxs=dict_users_train[idx])
w = local.train(net=copy.deepcopy(net_local).to(args.device), lr=lr)
w_locals.append(copy.deepcopy(w))
net_local_list[idx].load_state_dict(copy.deepcopy(w))
if (iter + 1) % args.test_freq == 0:
acc_test, loss_test = test_img_local_all(net_local_list, args, dataset_test, dict_users_test, return_all=False)
print('Round {:3d}, Test loss {:.3f}, Test accuracy: {:.2f}'.format(
iter, loss_test, acc_test))
if best_acc is None or acc_test > best_acc:
net_best = copy.deepcopy(net_glob)
best_acc = acc_test
best_epoch = iter
for user_idx in range(args.num_users):
best_save_path = os.path.join(base_dir, algo_dir, 'best_local_{}.pt'.format(user_idx))
torch.save(net_local_list[user_idx].state_dict(), best_save_path)
results.append(np.array([iter, loss_test, acc_test, best_acc]))
final_results = np.array(results)
final_results = pd.DataFrame(final_results, columns=['epoch', 'loss_test', 'acc_test', 'best_acc'])
final_results.to_csv(results_save_path, index=False)
# rollback local parameters
net_local_list = copy.deepcopy(backup_net_local_list)
print('Best model, iter: {}, acc: {}'.format(best_epoch, best_acc))