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502 lines (421 loc) · 21.1 KB
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import numpy as np
import json
import torch
import torch.optim as optim
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from torch.autograd import Variable
import torch.utils.data as data
import argparse
import logging
import os
import copy
from math import *
import random
import datetime
#from torch.utils.tensorboard import SummaryWriter
from model import *
from utils import *
from vggmodel import *
from resnetcifar import *
from tqdm import tqdm
import argparse
import copy
import os
import warnings
# import torchvision.models as models
import numpy as np
###########here
# from helpers.datasets import partition_data
# from torch.utils.data import DataLoader, Dataset
from helpers.utils import average_weights, KLDiv, setup_seed, test #get_dataset,
##########
from helpers.synthesizers import AdvSynthesizer
from models.generator import Generator
from models.nets import CNNCifar, CNNMnist, CNNCifar100
import torch.nn.functional as F
from models.resnet import resnet18
# from models.vit import deit_tiny_patch16_224
# import wandb
warnings.filterwarnings('ignore')
upsample = torch.nn.Upsample(mode='nearest', scale_factor=7)
class LocalUpdate(object):
def __init__(self, args, dataloader):# ,#dataset, idxs):
self.args = args
self.train_loader= dataloader
# #self.train_loader = DataLoader(DatasetSplit(dataset, idxs),
# batch_size=self.args.local_bs, shuffle=True, num_workers=4)
def update_weights(self, model, client_id):
model.train()
optimizer = torch.optim.SGD(model.parameters(), lr=self.args.lr,
momentum=0.9)
# label_list = [0] * 100
# for batch_idx, (images, labels) in enumerate(self.train_loader):
# for i in range(100):
# label_list[i] += torch.sum(labels == i).item()
# print(label_list)
local_acc_list = []
for iter in tqdm(range(self.args.local_ep)):
for batch_idx, (images, labels) in enumerate(self.train_loader):
images, labels = images.cuda(), labels.cuda()
model.zero_grad()
# ---------------------------------------
output = model(images)
loss = F.cross_entropy(output, labels)
# ---------------------------------------
loss.backward()
optimizer.step()
acc, test_loss = test(model, test_loader)
# if client_id == 0:
# wandb.log({'local_epoch': iter})
# wandb.log({'client_{}_accuracy'.format(client_id): acc})
local_acc_list.append(acc)
return model.state_dict(), np.array(local_acc_list)
def args_parser():
parser = argparse.ArgumentParser()
# federated arguments (Notation for the arguments followed from paper)
parser.add_argument('--epochs', type=int, default=10,
help="number of rounds of training")
parser.add_argument('--num_users', type=int, default=5,
help="number of users: K")
parser.add_argument('--frac', type=float, default=1,
help='the fraction of clients: C')
parser.add_argument('--local_ep', type=int, default=100,
help="the number of local epochs: E")
parser.add_argument('--local_bs', type=int, default=64,
help="local batch size: B")
parser.add_argument('--lr', type=float, default=0.01,
help='learning rate')
parser.add_argument('--momentum', type=float, default=0.9,
help='SGD momentum (default: 0.5)')
# other arguments
parser.add_argument('--dataset', type=str, default='cifar10', help="name \
of dataset")
parser.add_argument('--iid', type=int, default=0,
help='Default set to non-IID. Set to 0 for non-IID.')
# Data Free
parser.add_argument('--adv', default=0, type=float, help='scaling factor for adv loss')
parser.add_argument('--bn', default=0, type=float, help='scaling factor for BN regularization')
parser.add_argument('--oh', default=0, type=float, help='scaling factor for one hot loss (cross entropy)')
parser.add_argument('--act', default=0, type=float, help='scaling factor for activation loss used in DAFL')
parser.add_argument('--save_dir', default='run/synthesis', type=str)
parser.add_argument('--partition', default='noniid-labeldir', type=str)
parser.add_argument('--beta', default=0.5, type=float,
help=' If beta is set to a smaller value, '
'then the partition is more unbalanced')
# Basic
parser.add_argument('--lr_g', default=1e-3, type=float,
help='initial learning rate for generation')
parser.add_argument('--T', default=1, type=float)
parser.add_argument('--g_steps', default=20, type=int, metavar='N',
help='number of iterations for generation')
parser.add_argument('--batch_size', default=64, type=int, metavar='N',
help='number of total iterations in each epoch')
parser.add_argument('--nz', default=256, type=int, metavar='N',
help='number of total iterations in each epoch')
parser.add_argument('--synthesis_batch_size', default=256, type=int)
# Misc
parser.add_argument('--seed', default=None, type=int,
help='seed for initializing training.')
parser.add_argument('--type', default="pretrain", type=str,
help='seed for initializing training.')
parser.add_argument('--model', default="", type=str,
help='seed for initializing training.')
parser.add_argument('--datadir', type=str, required=False, default="./data/", help="Data directory")
parser.add_argument('--other', default="", type=str,
help='seed for initializing training.')
parser.add_argument('--logdir', type=str, required=False, default="./logs/", help='Log directory path')
args = parser.parse_args()
return args
class Ensemble(torch.nn.Module):
def __init__(self, model_list):
super(Ensemble, self).__init__()
self.models = model_list
def forward(self, x):
logits_total = 0
for i in range(len(self.models)):
logits = self.models[i](x)
logits_total += logits
logits_e = logits_total / len(self.models)
return logits_e
def kd_train(synthesizer, model, criterion, optimizer):
student, teacher = model
student.train()
teacher.eval()
description = "loss={:.4f} acc={:.2f}%"
total_loss = 0.0
correct = 0.0
with tqdm(synthesizer.get_data()) as epochs:
for idx, (images) in enumerate(epochs):
optimizer.zero_grad()
images = images.cuda()
with torch.no_grad():
t_out = teacher(images)
s_out = student(images.detach())
loss_s = criterion(s_out, t_out.detach())
loss_s.backward()
optimizer.step()
total_loss += loss_s.detach().item()
avg_loss = total_loss / (idx + 1)
pred = s_out.argmax(dim=1)
target = t_out.argmax(dim=1)
correct += pred.eq(target.view_as(pred)).sum().item()
acc = correct / len(synthesizer.data_loader.dataset) * 100
epochs.set_description(description.format(avg_loss, acc))
def save_checkpoint(state, is_best, filename='checkpoint.pth'):
if is_best:
torch.save(state, filename)
def get_model(args):
if args.model == "mnist_cnn":
global_model = SimpleCNNMNIST(input_dim=(16 * 4 * 4), hidden_dims=[120, 84], output_dim=10).cuda()
#global_model = CNNMnist().cuda()
elif args.model == "fmnist_cnn":
global_model = SimpleCNNMNIST(input_dim=(16 * 4 * 4), hidden_dims=[120, 84], output_dim=10).cuda()
#global_model = CNNMnist().cuda()
elif args.model == "cnn":
global_model = SimpleCNN(input_dim=(16 * 5 * 5), hidden_dims=[120, 84], output_dim=10).cuda()
#global_model = CNNCifar().cuda()
elif args.model == "svhn_cnn":
global_model = SimpleCNN(input_dim=(16 * 5 * 5), hidden_dims=[120, 84], output_dim=10).cuda()
#global_model = CNNCifar().cuda()
elif args.model == "cifar100_cnn":
global_model = SimpleCNN(input_dim=(16 * 5 * 5), hidden_dims=[120, 84], output_dim=100).cuda()
#global_model = CNNCifar100().cuda()
elif args.model == "res":
# global_model = resnet18()
global_model = resnet18(num_classes=10).cuda() #10
elif args.model == "vgg-9":
if args.dataset in ("mnist", 'femnist'):
net = ModerateCNNMNIST()
elif args.dataset in ("cifar10", "cinic10", "svhn"):
# print("in moderate cnn")
net = ModerateCNN()
elif args.dataset == 'celeba':
net = ModerateCNN(output_dim=2)
# elif args.model == "vit":
# global_model = deit_tiny_patch16_224(num_classes=1000,
# drop_rate=0.,
# drop_path_rate=0.1)
# global_model.head = torch.nn.Linear(global_model.head.in_features, 10)
# global_model = global_model.cuda()
# global_model = torch.nn.DataParallel(global_model)
return global_model
if __name__ == '__main__':
# torch.set_printoptions(profile="full")
args = args_parser()
mkdirs(args.logdir)
argument_path='dense_experiment_arguments-%s.json' % datetime.datetime.now().strftime("%Y-%m-%d-%H:%M-%S")
with open(os.path.join(args.logdir, argument_path), 'w') as f:
json.dump(str(args), f)
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
log_path='dense_'+args.partition+'_'+str(args.beta)+'_dense_experiment_log-%s' % (datetime.datetime.now().strftime("%Y-%m-%d-%H:%M-%S")) +'.log'
logging.basicConfig(
filename=os.path.join(args.logdir, log_path),
# filename='/home/qinbin/test.log',
format='%(asctime)s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M', level=logging.DEBUG, filemode='w')
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
seed = args.seed
logger.info(args)
logger.info("#" * 100)
np.random.seed(seed)
torch.manual_seed(seed)
random.seed(seed)
logger.info("Partitioning data")
X_train, y_train, X_test, y_test, net_dataidx_map, traindata_cls_counts = partition_data(args.dataset, args.datadir, args.logdir, args.partition, args.num_users, beta=args.beta)
n_classes = len(np.unique(y_train))
train_dl_global, test_dl_global, train_ds_global, test_ds_global = get_dataloader(args.dataset,
args.datadir,
args.batch_size,
32)
print("len train_dl_global:", len(train_ds_global))
data_size = len(test_ds_global)
train_all_in_list = []
test_all_in_list = []
logger.info("Initializing nets")
#============
# train_dataset, test_dataset, user_groups, traindata_cls_counts = partition_data(
# args.dataset, args.partition, beta=args.beta, num_users=args.num_users)
#
# test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=256,
# shuffle=False, num_workers=4)
test_loader=test_dl_global
# BUILD MODEL
global_model = get_model(args)
bst_acc = -1
description = "inference acc={:.4f}% loss={:.2f}, best_acc = {:.2f}%"
local_weights = []
global_model.train()
acc_list = []
users = []
if args.type == "pretrain":
# ===============================================
for idx in range(args.num_users):
print("client {}".format(idx))
users.append("client_{}".format(idx))
net_id=idx
dataidxs = net_dataidx_map[net_id]
noise_level = 0 / (args.num_users - 1) * net_id
train_dl_local, test_dl_local, _, _ = get_dataloader(args.dataset, args.datadir, args.batch_size, 32,
dataidxs, noise_level)
#local_model = LocalUpdate(args=args, dataset=train_dataset,
# idxs=user_groups[idx])
local_model = LocalUpdate(args=args,dataloader=train_dl_local)
w, local_acc = local_model.update_weights(copy.deepcopy(global_model), idx)
acc_list.append(local_acc)
local_weights.append(copy.deepcopy(w))
# wandb
# for i in range(args.local_ep):
# logger.info({"client_{}_acc".format(users[0]):acc_list[0][i],
# "client_{}_acc".format(users[1]):acc_list[1][i],
# "client_{}_acc".format(users[2]):acc_list[2][i],
# "client_{}_acc".format(users[3]):acc_list[3][i],
# "client_{}_acc".format(users[4]):acc_list[4][i],
# })
# np.save("client_{}_acc.npy".format(args.num_users), acc_list)
# logger.info({"client_accuracy" : wandb.plot.line_series(
# xs=[ i for i in range(args.local_ep) ],
# ys=[ acc_list[i] for i in range(args.num_users) ],
# keys=users,
# title="Client Accuacy")})
# torch.save(local_weights, '{}_{}.pkl'.format(name, iid))
print("###~~~")
torch.save(local_weights, '{}_{}clients_{}.pkl'.format(args.dataset, args.num_users, args.beta))
print("###~~~")
# update global weights by FedAvg
# global_weights = average_weights(local_weights)
# global_model.load_state_dict(global_weights)
# logger.info("avg acc:")
# test_acc, test_loss = test(global_model, test_loader)
# model_list = []
# for i in range(len(local_weights)):
# net = copy.deepcopy(global_model)
# net.load_state_dict(local_weights[i])
# model_list.append(net)
# ensemble_model = Ensemble(model_list)
# logger.info("ensemble acc:")
# test(ensemble_model, test_loader)
#global_weights = average_weights(local_weights)
#global_model.load_state_dict(global_weights)
#logger.info("avg acc:")
#test_acc, test_loss = test(global_model, test_loader)
#logger.info(test_acc)
#model_list = []
#for i in range(len(local_weights)):
# net = copy.deepcopy(global_model)
# net.load_state_dict(local_weights[i])
# model_list.append(net)
#ensemble_model = Ensemble(model_list)
#logger.info("ensemble acc:")
#acc, test_loss=test(ensemble_model, test_loader)
#logger.info(acc)
# ===============================================
else:
# ===============================================
local_weights = torch.load('{}_{}clients_{}.pkl'.format(args.dataset, args.num_users, args.beta))
global_weights = average_weights(local_weights)
global_model.load_state_dict(global_weights)
logger.info("avg acc:")
test_acc, test_loss = test(global_model, test_loader)
model_list = []
for i in range(len(local_weights)):
net = copy.deepcopy(global_model)
net.load_state_dict(local_weights[i])
model_list.append(net)
ensemble_model = Ensemble(model_list)
logger.info("ensemble acc:")
test(ensemble_model, test_loader)
# ===============================================
global_model = get_model(args)
# ===============================================
# data generator
nz = args.nz
nc = 3 if "cifar" in args.dataset or args.dataset == "svhn" else 1
img_size = 32 if "cifar" in args.dataset or args.dataset == "svhn" else 28
generator = Generator(nz=nz, ngf=64, img_size=img_size, nc=nc).cuda()
args.cur_ep = 0
img_size2 = (3, 32, 32) if "cifar" in args.dataset or args.dataset == "svhn" else (1, 28, 28)
num_class = 100 if args.dataset == "cifar100" else 10
synthesizer = AdvSynthesizer(ensemble_model, model_list, global_model, generator,
nz=nz, num_classes=num_class, img_size=img_size2,
iterations=args.g_steps, lr_g=args.lr_g,
synthesis_batch_size=args.synthesis_batch_size,
sample_batch_size=args.batch_size,
adv=args.adv, bn=args.bn, oh=args.oh,
save_dir=args.save_dir, dataset=args.dataset)
# &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
criterion = KLDiv(T=args.T)
optimizer = torch.optim.SGD(global_model.parameters(), lr=args.lr,
momentum=0.9)
global_model.train()
distill_acc = []
for epoch in tqdm(range(args.epochs)):
# 1. Data synthesis
synthesizer.gen_data(args.cur_ep) # g_steps
args.cur_ep += 1
kd_train(synthesizer, [global_model, ensemble_model], criterion, optimizer) # # kd_steps
acc, test_loss = test(global_model, test_loader)
distill_acc.append(acc)
is_best = acc > bst_acc
bst_acc = max(acc, bst_acc)
_best_ckpt = 'df_ckpt/{}.pth'.format(args.other)
logger.info("best acc:{}".format(bst_acc))
# save_checkpoint({
# 'state_dict': global_model.state_dict(),
# 'best_acc': float(bst_acc),
# }, is_best, _best_ckpt)
logger.info({'accuracy': acc})
logger.info({'best accuracy': bst_acc})
# logger.info({"global_accuracy" :
# xs=[ i for i in range(args.epochs) ],
# ys=distill_acc,
# keys="DENSE",
# title="Accuacy of DENSE"})
# np.save("distill_acc_{}.npy".format(args.dataset), np.array(distill_acc))
# ===============================================
#==============
# python3 experiments_dense.py --type=pretrain --lr=0.01 --model=cnn --dataset=cifar10 --beta=0.001 \
# --seed=0 --num_users=2 --local_ep=1 --epochs=200 --partition noniid-labeldir
# python3 experiments_dense.py --type=kd_train --epochs=2 --lr=0.005 --batch_size 64 \
# --synthesis_batch_size=256 --g_steps 30 --lr_g 1e-3 --bn 1.0 --oh 1.0 --T 20 --save_dir=run/cifar10 \
# --other=cifar10 --model=cnn --dataset=cifar10 --adv=1 --beta=0.001 --seed=1 --num_users 2 --partition noniid-labeldir
#
#
#
# python3 experiments_dense.py --type=pretrain --lr=0.01 --model=cifar100_cnn --dataset=cifar100 \
# --beta=0.001 --seed=0 --num_users=2 --local_ep=1 --epochs=200 --partition noniid-labeldir
# python3 experiments_dense.py --type=kd_train --epochs=2 --lr=0.005 --batch_size 64 \
# --synthesis_batch_size=256 --g_steps 30 --lr_g 1e-3 --bn 1.0 --oh 1.0 --T 20 --save_dir=run/cifar100 \
# --other=cifar100 --model=cifar100_cnn --dataset=cifar100 --adv=1 --beta=0.001 --seed=1 --num_users 2 --partition noniid-labeldir
#
#
#
# python3 experiments_dense.py --type=pretrain --lr=0.01 --model=svhn_cnn --dataset=svhn \
# --beta=0.001 --seed=0 --num_users=2 --local_ep=1 --epochs=200 --partition noniid-labeldir
# python3 experiments_dense.py --type=kd_train --epochs=2 --lr=0.005 --batch_size 64 \
# --synthesis_batch_size=256 --g_steps 30 --lr_g 1e-3 --bn 1.0 --oh 1.0 --T 20 --save_dir=run/svhn \
# --other=svhn --model=svhn_cnn --dataset=svhn --adv=1 --beta=0.001 --seed=1 --num_users 2 --partition noniid-labeldir
#
#
# python3 experiments_dense.py --type=pretrain --lr=0.01 --model=mnist_cnn --dataset=mnist \
# --beta=0.001 --seed=0 --num_users=2 --local_ep=1 --epochs=200 --partition noniid-labeldir
# python3 experiments_dense.py --type=kd_train --epochs=2 --lr=0.005 --batch_size 64 \
# --synthesis_batch_size=256 --g_steps 30 --lr_g 1e-3 --bn 1.0 --oh 1.0 --T 20 --save_dir=run/mnist \
# --other=mnist --model=mnist_cnn --dataset=mnist --adv=1 --beta=0.001 --seed=1 --num_users 2 --partition noniid-labeldir
#
#
# python3 experiments_dense.py --type=pretrain --lr=0.01 --model=fmnist_cnn \
# --dataset=fmnist --beta=0.001 --seed=0 --num_users=2 --local_ep=1 --epochs=200 --partition noniid-labeldir
# python3 experiments_dense.py --type=kd_train --epochs=2 --lr=0.005 --batch_size 64 \
# --synthesis_batch_size=256 --g_steps 30 --lr_g 1e-3 --bn 1.0 --oh 1.0 --T 20 --save_dir=run/fmnist \
# --other=fmnist --model=fmnist_cnn --dataset=fmnist --adv=1 --beta=0.001 --seed=1 --num_users 2 --partition noniid-labeldir
#
#
# python3 experiments_dense.py --type=pretrain --lr=0.01 --model=cnn --dataset=cifar10 --beta=0.001 \
# --seed=0 --num_users=10 --local_ep=1 --epochs=200 --partition noniid-#label1
# python3 experiments_dense.py --type=kd_train --epochs=2 --lr=0.005 --batch_size 64 \
# --synthesis_batch_size=256 --g_steps 30 --lr_g 1e-3 --bn 1.0 --oh 1.0 --T 20 --save_dir=run/cifar10 \
# --other=cifar10 --model=cnn --dataset=cifar10 --adv=1 --beta=0.001 --seed=1 --num_users 10 --partition noniid-#label1