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federated_train.py
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import numpy as np
import torch
from VGG import vgg
from resnet import *
import time
import random
from utils import *
from partition import *
from loaders import *
from torch.utils.tensorboard import SummaryWriter
from traintest import *
from aggregation import *
import os
def print_cuda_info():
print("Is cuda available?", torch.cuda.is_available())
print("Is cuDNN version:", torch.backends.cudnn.version())
print("cuDNN enabled? ", torch.backends.cudnn.enabled)
print("Device count?", torch.cuda.device_count())
print("Current device?", torch.cuda.current_device())
print("Device name? ", torch.cuda.get_device_name(torch.cuda.current_device()))
def seed_everything(SEED):
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.cuda.manual_seed_all(SEED)
torch.backends.cudnn.deterministic = True
os.environ['PYTHONHASHSEED'] = str(SEED)
def remove_unwanted(directory):
for filename in os.listdir(directory):
f = os.path.join(directory, filename)
if os.path.isfile(f):
if ('test' in f):
pass
else:
if 'pth.tar' in f:
os.remove(f)
print(f)
def main(total_epochs, evaluate, path, splits, t_round, wd, normalization, n_clients, seed, count, lr, dataset,
partition, mod, depth, mode, coef_t, coef_d,
external_distillation, save_path, layer, average, common_dataset_size,
loss_type, path_s, beta,
multiloss, scale, multiloss_type, incremental_step, mu, temperature,
incremental_rounds, incremental_validation_type, model_buffer_size, exemplar_percentage):
writer = SummaryWriter(log_dir='./' + save_path)
print(splits)
seed_everything(seed)
print('Local epochs are ' + str(t_round))
checkpoint_prev = 0
batch_size = 64
lr_next = np.zeros(n_clients)
train_loader_list, valid_loader_list, test_loader, n_classes, valid_loader_server, train_set, valid_set, valid_dataset_server, testSet = \
loader_build(dataset, mode, partition, splits, n_clients, beta, batch_size, common_dataset_size)
server_exemplar_size = int(len(valid_loader_server.dataset.indices) * exemplar_percentage)
client_train_exemplar_size = int(len(train_loader_list[0].dataset.indices) * exemplar_percentage)
client_valid_exemplar_size = int(len(valid_loader_list[0].dataset.indices) * exemplar_percentage)
if evaluate == True:
evaluate(test_loader, path, mod, normalization, n_classes, depth)
momentum = 0.9
weight_decay = wd
if incremental_step > 0:
total_epochs = t_round * incremental_rounds * n_classes // incremental_step
n_rounds = total_epochs // t_round
if n_rounds * t_round < total_epochs:
n_rounds += 1
total_epochs = n_rounds * t_round
if external_distillation:
extracted_logits = external_distillation(mod, normalization, depth, layer, n_classes, average, n_clients,
train_loader_list, multiloss, scale)
else:
extracted_logits = [0]
best_prec, best_round, model, lr_next, round_n, test_r_loss, test_r_prec = communication_round_train(writer, n_rounds,n_clients,
n_classes,normalization,
mod, dataset,depth,momentum,weight_decay,lr, t_round,
save_path,mode,train_loader_list,valid_loader_list,total_epochs,
count, coef_t,coef_d,lr_next,checkpoint_prev,extracted_logits,
average, external_distillation,valid_loader_server,
common_dataset_size,layer,loss_type,
path_s, multiloss,scale,multiloss_type,incremental_step,
train_set,valid_set,batch_size,valid_dataset_server,server_exemplar_size,
client_train_exemplar_size,client_valid_exemplar_size,mu,temperature,
incremental_rounds,incremental_validation_type, model_buffer_size,
testSet,test_loader)
writer.close()
if (mode == 'traditional'):
model.cuda()
checkpoint = load_checkpoint(best_prec, str(count) + '_' + str(best_round), n_clients, save_path)
model.load_state_dict(checkpoint['state_dict'])
loss_server, prec_server = test(model, test_loader)
save_checkpoint({
'epoch': round_n + 1,
'state_dict': model.state_dict(),
'best_prec': prec_server,
'variation': 0,
'optimizer': 0,
}, True, 'test_result_' + str(count),
n_clients, filepath=save_path)
print('The precision of the server at test set is ' + str(prec_server))
else:
valid_prec_clients = torch.zeros(n_clients)
if mod == 'vgg':
model = vgg(normalization, n_classes, depth=depth)
elif mod == 'resnet':
model = resnet(n_classes=n_classes, depth=depth)
model.cuda()
f = open(save_path + "/client_model_results.txt", "w")
for i in range(n_clients):
checkpoint = load_checkpoint_local('local_model_' + str(i), save_path)
model.load_state_dict(checkpoint['state_dict'])
loss, prec = test(model, test_loader)
f.write(str(prec) + '\n')
valid_prec_clients[i] = prec
f.close()
f = open(save_path + "/incremental_per_round_loss.txt", "w")
f.write(str(test_r_loss) + '\n')
f.close()
f = open(save_path + "/incremental_per_round_prec.txt", "w")
f.write(str(test_r_prec) + '\n')
f.close()
prec_server = torch.mean(valid_prec_clients).item()
variation = torch.std(valid_prec_clients).item()
save_checkpoint({
'epoch': round_n + 1,
'state_dict': 0,
'best_prec': prec_server,
'variation': variation,
'optimizer': 0,
}, True, 'test_result_' + str(count),
n_clients, filepath=save_path)
model.cpu()
remove_unwanted(save_path)
return prec_server
if __name__ == '__main__':
path_s = '0'
torch.cuda.set_device(int(path_s))
torch.cuda.synchronize()
print_cuda_info()
argument_dict = {}
argument_dict['path'] = ''
argument_dict['wd_factor'] = 1e-4
argument_dict['normalization'] = 'normal'
argument_dict['epochs'] = 300
argument_dict['clients'] = 10
argument_dict['t_round'] = 10
argument_dict['lr'] = 0.1
argument_dict['dataset'] = 'cifar100'
argument_dict['mod'] = 'resnet'
argument_dict['depth'] = 56
count = 0
iters = 1
argument_dict['layer'] = 'last'
argument_dict['mode'] = 'distillation'
argument_dict['common_dataset_size'] = 0.2
argument_dict['coef_t'] = 0
argument_dict['coef_d'] = 1
argument_dict['external_distillation'] = ''
argument_dict['average'] = False
argument_dict['loss'] = 'mse'
argument_dict['temperature'] = 0.5
argument_dict['exemplar_percentage'] = 0.2
argument_dict['incremental_step'] = 20
argument_dict['incremental_rounds'] = 6
argument_dict['incremental_validation_type'] = 'b'
argument_dict['model_buffer_size'] = 4
argument_dict['mu'] = 0.5
argument_dict['beta'] = 0.5
argument_dict['partition'] = 'random_split'
argument_dict['multiloss'] = True
argument_dict['multiloss_type'] = 'b'
argument_dict['scale'] = 1
for j in range(iters):
splits = create_splits(argument_dict)
save_path = create_save_folder(argument_dict, path_s)
main(argument_dict['epochs'], False, argument_dict['path'], splits, argument_dict['t_round'],
argument_dict['wd_factor'], argument_dict['normalization']
, argument_dict['clients'], j, count, argument_dict['lr'], argument_dict['dataset'],
argument_dict['partition'], argument_dict['mod'],
argument_dict['depth'], argument_dict['mode'], argument_dict['coef_t'], argument_dict['coef_d'],
argument_dict['external_distillation'],
save_path, argument_dict['layer'], argument_dict['average'],
argument_dict['common_dataset_size'], argument_dict['loss'],
path_s, argument_dict['beta'],
argument_dict['multiloss'], argument_dict['scale'],
argument_dict['multiloss_type'], argument_dict['incremental_step'],
argument_dict['mu'],
argument_dict['temperature'], argument_dict['incremental_rounds'],
argument_dict['incremental_validation_type'], argument_dict['model_buffer_size'],
argument_dict['exemplar_percentage'])
count += 1