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run_experiment.py
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run_experiment.py
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"""Run Experiment
This script allows to run one federated learning experiment; the experiment name, the method and the
number of clients/tasks should be precised along side with the hyper-parameters of the experiment.
The results of the experiment (i.e., training logs) are written to ./logs/ folder.
This file can also be imported as a module and contains the following function:
* run_experiment - runs one experiments given its arguments
"""
import os.path
from torch.utils.tensorboard import SummaryWriter
from utils.args import *
from utils.utils import *
import pickle
def init_clients(args_, root_path, logs_dir, save_path):
"""
initialize clients from data folders
:param args_:
:param root_path: path to directory containing data folders
:param logs_dir: path to logs root
:return: List[Client]
"""
print("===> Building data iterators..")
train_iterators, val_iterators, test_iterators = \
get_loaders(
type_=LOADER_TYPE[args_.experiment],
root_path=root_path,
batch_size=args_.bz,
is_validation=args_.validation,
dist_shift=args.dist_shift,
dp = args.dp,
emb_size= args_.embedding_dimension
)
# all_data_tensor = []
# for cur_data in train_iterators:
# all_data_tensor.append(cur_data.dataset.data)
# all_data_tensor = torch.cat(all_data_tensor, dim=0)
#
# model = models.resnet18(pretrained=True)
#
# del model.fc
# all_data_tensor = all_data_tensor.view(-1,1,28,28)
# x = all_data_tensor
# if all_data_tensor.shape[1] == 1:
# x = all_data_tensor.repeat(1, 3, 1, 1)
# x = model.conv1(x.float())
# x = model.bn1(x)
# x = model.relu(x)
# x = model.maxpool(x)
#
# x = model.layer1(x)
# x = model.layer2(x)
# x = model.layer3(x)
# x = model.layer4(x)
#
# # Extract the feature maps produced by the encoder
# encoder_output = x.squeeze()
# U, S, V = torch.svd(encoder_output)
# global PCA_V
# PCA_V = V
# print(PCA_V.size())
# with open(f"data/mnist9/all_data/PCA.pkl" , 'wb') as f:
# pickle.dump(PCA_V, f)
# raise
# encoder_output = encoder_output.view(encoder_output.size(0), -1)
# pca_transformer = PCA(n_components=emb_size)
# # Fit the PCA transformer to your data
#
# X_pca = pca_transformer.fit_transform(encoder_output.detach().numpy())
# # Convert the resulting principal components to a PyTorch tensor
# projected = torch.from_numpy(X_pca).float().cuda()
print("===> Initializing clients..")
clients_ = []
for task_id, (train_iterator, val_iterator, test_iterator) in \
enumerate(tqdm(zip(train_iterators, val_iterators, test_iterators), total=len(train_iterators))):
# if train_iterator is None or test_iterator is None:
# continue
# if
learners_ensemble =\
get_learners_ensemble(
n_learners=args_.n_learners,
client_type=CLIENT_TYPE[args_.method],
name=args_.experiment,
device=args_.device,
optimizer_name=args_.optimizer,
scheduler_name=args_.lr_scheduler,
initial_lr=args_.lr,
input_dim=args_.input_dimension,
output_dim=args_.output_dimension,
n_rounds=args_.n_rounds,
seed=args_.seed,
mu=args_.mu,
embedding_dim=args_.embedding_dimension,
n_gmm=args_.n_gmm
)
# learners_ensemble.load_state("saves/femnist_unseen_base/FedGMM_sanity/global_ensemble.pt")
logs_path = os.path.join(logs_dir, "task_{}".format(task_id))
os.makedirs(logs_path, exist_ok=True)
logger = SummaryWriter(logs_path)
save_path_ = os.path.join(save_path, "task_{}".format(task_id))
client = get_client(
client_type=CLIENT_TYPE[args_.method],
learners_ensemble=learners_ensemble,
q=args_.q,
train_iterator=train_iterator,
val_iterator=val_iterator,
test_iterator=test_iterator,
logger=logger,
local_steps=args_.local_steps,
save_path=save_path_,
tune_locally=args_.locally_tune_clients
)
clients_.append(client)
return clients_
def run_experiment(args_):
torch.manual_seed(args_.seed)
data_dir = get_data_dir(args_.experiment, unseen=False)
save_dir = get_save_dir(args_.experiment)
if "logs_dir" in args_:
logs_dir = args_.logs_dir
else:
logs_dir = os.path.join("logs", args_to_string(args_))
print("==> Clients initialization..")
clients = init_clients(args_,
root_path=os.path.join(data_dir, "train"),
logs_dir=os.path.join(logs_dir, "train"),
save_path=os.path.join(save_dir, "train"),
)
print("==> Test Clients initialization..")
test_clients = init_clients(args_, root_path=os.path.join(data_dir, "test"),
logs_dir=os.path.join(logs_dir, "test"),
save_path=os.path.join(save_dir, "test"))
logs_path = os.path.join(logs_dir, "train", "global")
os.makedirs(logs_path, exist_ok=True)
global_train_logger = SummaryWriter(logs_path)
logs_path = os.path.join(logs_dir, "test", "global")
os.makedirs(logs_path, exist_ok=True)
global_test_logger = SummaryWriter(logs_path)
global_learners_ensemble = \
get_learners_ensemble(
n_learners=args_.n_learners,
client_type=CLIENT_TYPE[args_.method],
name=args_.experiment,
device=args_.device,
optimizer_name=args_.optimizer,
scheduler_name=args_.lr_scheduler,
initial_lr=args_.lr,
input_dim=args_.input_dimension,
output_dim=args_.output_dimension,
n_rounds=args_.n_rounds,
seed=args_.seed,
mu=args_.mu,
embedding_dim=args_.embedding_dimension,
n_gmm=args_.n_gmm,
)
if args_.decentralized:
aggregator_type = 'decentralized'
else:
aggregator_type = AGGREGATOR_TYPE[args_.method]
# global_learners_ensemble.load_state("saves/femnist_unseen_base/FedGMM_sanity/global_ensemble.pt")
aggregator =\
get_aggregator(
aggregator_type=aggregator_type,
clients=clients,
global_learners_ensemble=global_learners_ensemble,
lr_lambda=args_.lr_lambda,
lr=args_.lr,
q=args_.q,
mu=args_.mu,
communication_probability=args_.communication_probability,
sampling_rate=args_.sampling_rate,
log_freq=args_.log_freq,
global_train_logger=global_train_logger,
global_test_logger=global_test_logger,
test_clients=test_clients,
verbose=args_.verbose,
em_step=args.em_step,
seed=args_.seed
)
if "save_dir" in args_:
save_dir = os.path.join(args_.save_dir)
os.makedirs(save_dir, exist_ok=True)
aggregator.save_state(save_dir)
print("Training..")
pbar = tqdm(total=args_.n_rounds)
current_round = 0
gmm = False
while current_round <= args_.n_rounds:
# aggregator.update_clients()
# aggregator.write_log()
# raise
if aggregator_type == "ACGcentralized":
aggregator.mix()
else:
aggregator.mix()
if current_round % 10 == 0:
aggregator.save_state(save_dir)
print("saved at epoch", current_round)
if aggregator.c_round != current_round:
pbar.update(1)
current_round = aggregator.c_round
print(current_round)
# if current_round > 50:
# gmm=False
global_train_logger.flush()
global_test_logger.flush()
global_train_logger.close()
global_test_logger.close()
if __name__ == "__main__":
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
args = parse_args()
run_experiment(args)