def load_omniglot_experiment_datasets(context: ReptileExperimentContext):
# Load and prepare Omniglot data
data_dir = REPO_ROOT / 'data' / 'omniglot'
omniglot_train_datasets, omniglot_test_datasets = load_omniglot_datasets(
str(data_dir.absolute()),
num_clients_train=context.num_clients_train,
num_clients_test=context.num_clients_test,
num_classes_per_client=context.num_classes_per_client,
num_shots_per_class=context.num_shots_per_class,
inner_batch_size=context.inner_batch_size)
return omniglot_train_datasets, omniglot_test_datasets
def run_reptile_experiment(
name,
dataset,
swap_labels,
classes,
shots,
seed,
model_class,
sgd,
adam_betas,
num_clients_train,
num_clients_test,
meta_batch_size,
num_meta_steps,
meta_learning_rate_initial,
meta_learning_rate_final,
eval_interval,
num_eval_clients_training,
do_final_evaluation,
num_eval_clients_final,
inner_batch_size,
inner_learning_rate,
num_inner_steps,
num_inner_steps_eval,
mean=None,
std=None
):
fix_random_seeds(seed)
fed_dataset_test = None
if dataset == 'femnist':
fed_dataset_train = load_femnist_dataset(
data_dir=str((REPO_ROOT / 'data').absolute()),
num_clients=num_clients_train,
batch_size=inner_batch_size,
random_seed=seed
)
elif dataset == 'omniglot':
fed_dataset_train, fed_dataset_test = load_omniglot_datasets(
data_dir=str((REPO_ROOT / 'data' / 'omniglot').absolute()),
num_clients_train=num_clients_train,
num_clients_test=num_clients_test,
num_classes_per_client=classes,
num_shots_per_class=shots,
inner_batch_size=inner_batch_size,
random_seed=seed
)
elif dataset == 'ham10k':
fed_dataset_train = load_ham10k_federated(partitions=num_clients_train, batch_size=inner_batch_size, mean=mean, std=std)
else:
raise ValueError(f'dataset "{dataset}" unknown')
#data_distribution_logged = False
for lr in inner_learning_rate:
for _is in num_inner_steps:
reptile_context = ReptileExperimentContext(
name=name,
dataset_name=dataset,
swap_labels=swap_labels,
num_classes_per_client=classes,
num_shots_per_class=shots,
seed=seed,
model_class=model_class,
sgd=sgd,
adam_betas=adam_betas,
num_clients_train=num_clients_train,
num_clients_test=num_clients_test,
meta_batch_size=meta_batch_size,
num_meta_steps=num_meta_steps,
meta_learning_rate_initial=meta_learning_rate_initial,
meta_learning_rate_final=meta_learning_rate_final,
eval_interval=eval_interval,
num_eval_clients_training=num_eval_clients_training,
do_final_evaluation=do_final_evaluation,
num_eval_clients_final=num_eval_clients_final,
inner_batch_size=inner_batch_size,
inner_learning_rate=lr,
num_inner_steps=_is,
num_inner_steps_eval=_is
)
experiment_specification = f'{reptile_context}'
experiment_logger = create_tensorboard_logger(
reptile_context.name, experiment_specification
)
reptile_context.experiment_logger = experiment_logger
log_after_round_evaluation_fns = [
partial(log_after_round_evaluation, experiment_logger)
]
run_reptile(
context=reptile_context,
dataset_train=fed_dataset_train,
dataset_test=fed_dataset_test,
initial_model_state=None,
after_round_evaluation=log_after_round_evaluation_fns
)
def run_reptile(context: ExperimentContext, initial_model_state=None):
num_clients_train = 10000
num_clients_test = 1000
num_classes_per_client = 5
num_shots_per_class = 5
eval_iters = 10
reptile_args = ReptileTrainingArgs(model=OmniglotLightning,
inner_optimizer=optim.Adam,
inner_learning_rate=0.001,
num_inner_steps=5,
num_inner_steps_eval=50,
log_every_n_steps=3,
inner_batch_size=10,
meta_batch_size=5,
meta_learning_rate_initial=1,
meta_learning_rate_final=0,
num_meta_steps=3000)
experiment_logger = create_tensorboard_logger(
context.name, "dataloading_ours;models_ours")
# Load and prepare Omniglot data
data_dir = REPO_ROOT / 'data' / 'omniglot'
#######
tf.disable_eager_execution()
#######
omniglot_train_clients, omniglot_test_clients = load_omniglot_datasets(
str(data_dir.absolute()),
num_clients_train=num_clients_train,
num_clients_test=num_clients_test,
num_classes_per_client=num_classes_per_client,
num_shots_per_class=num_shots_per_class,
inner_batch_size=reptile_args.inner_batch_size,
random_seed=RANDOM_SEED)
# Prepare ModelArgs for task training
inner_optimizer_args = OptimizerArgs(
optimizer_class=reptile_args.inner_optimizer,
lr=reptile_args.inner_learning_rate,
betas=(0, 0.999))
inner_model_args = ModelArgs(model_class=reptile_args.model,
optimizer_args=inner_optimizer_args,
num_classes=num_classes_per_client)
dummy_optimizer_args = OptimizerArgs(optimizer_class=optim.SGD)
meta_model_args = ModelArgs(reptile_args.model,
dummy_optimizer_args,
num_classes=num_classes_per_client)
"""
# Set up clients
# Since we are doing meta-learning, we need separate sets of training and
# test clients
train_clients = []
for c in omniglot_train_clients.train_data_local_dict.keys():
client = ReptileClient(
client_id=str(c),
model_args=inner_model_args,
context=context,
train_dataloader=omniglot_train_clients.train_data_local_dict[c],
num_train_samples=omniglot_train_clients.data_local_train_num_dict[c],
test_dataloader=omniglot_train_clients.test_data_local_dict[c],
num_test_samples=omniglot_train_clients.data_local_test_num_dict[c],
lightning_logger=experiment_logger
)
checkpoint_callback = ModelCheckpoint(
filepath=str(client.get_checkpoint_path(suffix='cb').absolute()))
client.set_trainer_callbacks([checkpoint_callback])
train_clients.append(client)
test_clients = []
for c in omniglot_test_clients.train_data_local_dict.keys():
client = ReptileClient(
client_id=str(c),
model_args=inner_model_args,
context=context,
train_dataloader=omniglot_test_clients.train_data_local_dict[c],
num_train_samples=omniglot_test_clients.data_local_train_num_dict[c],
test_dataloader=omniglot_test_clients.test_data_local_dict[c],
num_test_samples=omniglot_test_clients.data_local_test_num_dict[c],
lightning_logger=experiment_logger
)
test_clients.append(client)
# Set up server
server = ReptileServer(
participant_name='initial_server',
model_args=meta_model_args,
context=context,
initial_model_state=initial_model_state
)"""
#torch_model = OmniglotModel(num_classes=num_classes_per_client)
torch_model = OmniglotLightning(participant_name='global_model',
**inner_model_args.kwargs)
#torch_optimizer = inner_optimizer_args.optimizer_class(
# torch_model.parameters(),
# **inner_optimizer_args.optimizer_kwargs
#)
reptile = Reptile(global_model=torch_model,
model_kwargs=inner_model_args.kwargs,
inner_iterations=reptile_args.num_inner_steps,
inner_iterations_eval=reptile_args.num_inner_steps_eval)
for i in range(reptile_args.num_meta_steps):
frac_done = i / reptile_args.num_meta_steps
cur_meta_step_size = frac_done * reptile_args.meta_learning_rate_final + (
1 - frac_done) * reptile_args.meta_learning_rate_initial
meta_batch = {
k: omniglot_train_clients.train_data_local_dict[k]
for k in cyclerange(
i * reptile_args.meta_batch_size %
len(omniglot_train_clients.train_data_local_dict), (i + 1) *
reptile_args.meta_batch_size %
len(omniglot_train_clients.train_data_local_dict),
len(omniglot_train_clients.train_data_local_dict))
}
reptile.train_step(meta_batch=meta_batch,
meta_step_size=cur_meta_step_size)
if i % eval_iters == 0:
accuracies = []
k = RANDOM.randrange(
len(omniglot_train_clients.train_data_local_dict))
train_train = omniglot_train_clients.train_data_local_dict[k]
train_test = omniglot_train_clients.test_data_local_dict[k]
k = RANDOM.randrange(
len(omniglot_test_clients.train_data_local_dict))
test_train = omniglot_test_clients.train_data_local_dict[k]
test_test = omniglot_test_clients.test_data_local_dict[k]
for train_dl, test_dl in [(train_train, train_test),
(test_train, test_test)]:
correct = reptile.evaluate(train_dl, test_dl)
accuracies.append(correct / num_classes_per_client)
print('batch %d: train=%f test=%f' %
(i, accuracies[0], accuracies[1]))
# Write to TensorBoard
experiment_logger.experiment.add_scalar(
'train-test/acc/{}/mean'.format('global_model'),
accuracies[0],
global_step=i)
experiment_logger.experiment.add_scalar(
'test-test/acc/{}/mean'.format('global_model'),
accuracies[1],
global_step=i)
"""
def create_client_batches(clients: List[ReptileClient],
batch_size: int) -> List[List[ReptileClient]]:
if batch_size == -1:
client_batches = [clients]
else:
client_batches = [
clients[i:i + batch_size]
for i in range(0, len(clients), batch_size)
]
return client_batches
num_clients_train = 10000
num_clients_test = 1000
num_classes_per_client = 5
num_shots_per_class = 5
eval_iters = 10
reptile_args = ReptileTrainingArgs(model=OmniglotModel,
inner_optimizer=optim.Adam,
inner_learning_rate=0.001,
num_inner_steps=5,
num_inner_steps_eval=50,
log_every_n_steps=3,
inner_batch_size=10,
meta_batch_size=5,
meta_learning_rate_initial=1,
meta_learning_rate_final=0,
num_meta_steps=3000)
experiment_logger = create_tensorboard_logger(
context.name, "dataloading_ours;models_ours")
# Load and prepare Omniglot data
data_dir = REPO_ROOT / 'data' / 'omniglot'
#######
tf.disable_eager_execution()
#######
omniglot_train_clients, omniglot_test_clients = load_omniglot_datasets(
str(data_dir.absolute()),
num_clients_train=num_clients_train,
num_clients_test=num_clients_test,
num_classes_per_client=num_classes_per_client,
num_shots_per_class=num_shots_per_class,
inner_batch_size=reptile_args.inner_batch_size,
random_seed=RANDOM_SEED)
# Prepare ModelArgs for task training
inner_optimizer_args = OptimizerArgs(
optimizer_class=reptile_args.inner_optimizer,
lr=reptile_args.inner_learning_rate,
betas=(0, 0.999))
inner_model_args = ModelArgs(reptile_args.model,
inner_optimizer_args,
num_classes=num_classes_per_client)
dummy_optimizer_args = OptimizerArgs(optimizer_class=optim.SGD)
meta_model_args = ModelArgs(reptile_args.model,
dummy_optimizer_args,
num_classes=num_classes_per_client)
"""
# Set up clients
# Since we are doing meta-learning, we need separate sets of training and
# test clients
train_clients = initialize_reptile_clients(context, train_datasets)
test_clients = initialize_reptile_clients(context, test_datasets)
# Set up server
server = ReptileServer(
participant_name='initial_server',
model_args=context.meta_model_args,
context=context,
initial_model_state=initial_model_state
)"""
torch_model = OmniglotModel(num_classes=num_classes_per_client)
torch_optimizer = inner_optimizer_args.optimizer_class(
torch_model.parameters(), **inner_optimizer_args.optimizer_kwargs)
reptile = Reptile(model=torch_model,
optimizer=torch_optimizer,
inner_iterations=reptile_args.num_inner_steps,
inner_iterations_eval=reptile_args.num_inner_steps_eval)
for i in range(reptile_args.num_meta_steps):
frac_done = i / reptile_args.num_meta_steps
cur_meta_step_size = frac_done * reptile_args.meta_learning_rate_final + (
1 - frac_done) * reptile_args.meta_learning_rate_initial
meta_batch = {
k: omniglot_train_clients.train_data_local_dict[k]
for k in cyclerange(
i * reptile_args.meta_batch_size %
len(omniglot_train_clients.train_data_local_dict), (i + 1) *
reptile_args.meta_batch_size %
len(omniglot_train_clients.train_data_local_dict),
len(omniglot_train_clients.train_data_local_dict))
}
reptile.train_step(meta_batch=meta_batch,
meta_step_size=cur_meta_step_size)
if i % eval_iters == 0:
accuracies = []
k = RANDOM.randrange(
len(omniglot_train_clients.train_data_local_dict))
train_train = omniglot_train_clients.train_data_local_dict[k]
train_test = omniglot_train_clients.test_data_local_dict[k]
k = RANDOM.randrange(
len(omniglot_test_clients.train_data_local_dict))
test_train = omniglot_test_clients.train_data_local_dict[k]
test_test = omniglot_test_clients.test_data_local_dict[k]
for train_dl, test_dl in [(train_train, train_test),
(test_train, test_test)]:
correct = reptile.evaluate(train_dl, test_dl)
accuracies.append(correct / num_classes_per_client)
print('batch %d: train=%f test=%f' %
(i, accuracies[0], accuracies[1]))
# Write to TensorBoard
experiment_logger.experiment.add_scalar(
'train-test/acc/{}/mean'.format('global_model'),
accuracies[0],
global_step=i)
experiment_logger.experiment.add_scalar(
'test-test/acc/{}/mean'.format('global_model'),
accuracies[1],
global_step=i)
"""
def run_reptile(context: str, initial_model_state=None):
args = argument_parser().parse_args()
RANDOM = random.Random(args.seed)
# TODO: Possibly implement logic using ReptileExperimentContext
reptile_args = ReptileTrainingArgs(
model_class=OmniglotLightning,
sgd=args.sgd,
inner_learning_rate=args.learning_rate,
num_inner_steps=args.inner_iters,
num_inner_steps_eval=args.eval_iters,
log_every_n_steps=3,
meta_learning_rate_initial=args.meta_step,
meta_learning_rate_final=args.meta_step_final,
num_classes_per_client=args.classes
)
experiment_logger = create_tensorboard_logger(
'reptile',
(
f"{context};seed{args.seed};"
f"train-clients{args.train_clients};"
f"{args.classes}-way{args.shots}-shot;"
f"ib{args.inner_batch}ii{args.inner_iters}"
f"ilr{str(args.learning_rate).replace('.', '')}"
f"ms{str(args.meta_step).replace('.', '')}"
f"mb{args.meta_batch}ei{args.eval_iters}"
f"{'sgd' if args.sgd else 'adam'}"
)
)
# Load and prepare Omniglot data
data_dir = REPO_ROOT / 'data' / 'omniglot'
omniglot_train_clients, omniglot_test_clients = load_omniglot_datasets(
str(data_dir.absolute()),
num_clients_train=args.train_clients,
num_clients_test=args.test_clients,
num_classes_per_client=args.classes,
num_shots_per_class=args.shots,
inner_batch_size=args.inner_batch,
random_seed=args.seed
)
# Set up clients
# Since we are doing meta-learning, we need separate sets of training and
# test clients
train_clients = initialize_clients(omniglot_train_clients, reptile_args.get_inner_model_args(), context,
experiment_logger)
test_clients = initialize_clients(omniglot_test_clients, reptile_args.get_inner_model_args(), context,
experiment_logger)
# Set up server
server = ReptileServer(
participant_name='initial_server',
model_args=reptile_args.get_meta_model_args(),
context=context, # TODO: Change to ReptileExperimentContext
initial_model_state=initial_model_state
)
# Perform training
for i in range(args.meta_iters):
if args.meta_batch == -1:
meta_batch = train_clients
else:
meta_batch = [
train_clients[k] for k in cyclerange(
i*args.meta_batch % len(train_clients),
(i+1)*args.meta_batch % len(train_clients),
len(train_clients)
)
]
# Meta training step
reptile_train_step(
aggregator=server,
participants=meta_batch,
inner_training_args=reptile_args.get_inner_training_args(),
meta_training_args=reptile_args.get_meta_training_args(
frac_done=i / args.meta_iters
)
)
# Evaluation on train and test clients
if i % args.eval_interval == 0:
# train-test set
# Pick one train client at random and test on it
k = RANDOM.randrange(len(train_clients))
client = [train_clients[k]]
reptile_train_step(
aggregator=server,
participants=client,
inner_training_args=reptile_args.get_inner_training_args(eval=True),
evaluation_mode=True
)
result = evaluate_local_models(participants=client)
experiment_logger.experiment.add_scalar(
'train-test/acc/{}/mean'.format('global_model'),
torch.mean(result.get('test/acc')),
global_step=i + 1
)
# test-test set
# Pick one test client at random and test on it
k = RANDOM.randrange(len(test_clients))
client = [test_clients[k]]
reptile_train_step(
aggregator=server,
participants=client,
inner_training_args=reptile_args.get_inner_training_args(eval=True),
evaluation_mode=True
)
result = evaluate_local_models(participants=client)
experiment_logger.experiment.add_scalar(
'test-test/acc/{}/mean'.format('global_model'),
torch.mean(result.get('test/acc')),
global_step=i + 1
)
logger.info('finished training round')
# Final evaluation on a sample of train/test clients
for label, client_set in zip(['Train', 'Test'], [train_clients, test_clients]):
eval_sample = RANDOM.sample(client_set, args.eval_samples)
reptile_train_step(
aggregator=server,
participants=eval_sample,
inner_training_args=reptile_args.get_inner_training_args(eval=True),
evaluation_mode=True
)
result = evaluate_local_models(participants=eval_sample)
log_loss_and_acc('global_model', result.get('test/loss'), result.get('test/acc'),
experiment_logger, global_step=args.meta_iters)
experiment_logger.experiment.add_scalar(
f'final_{label}_acc',
torch.mean(result.get('test/acc')),
global_step=0
)
print(f"{label} accuracy: {torch.mean(result.get('test/acc'))}")
def run_reptile(context: ExperimentContext, initial_model_state=None):
num_clients_train = 10000
num_clients_test = 1000
num_classes_per_client = 5
num_shots_per_class = 5
eval_iters = 10
reptile_args = ReptileTrainingArgs(model=OmniglotModel,
inner_optimizer=optim.Adam,
inner_learning_rate=0.001,
num_inner_steps=5,
num_inner_steps_eval=50,
log_every_n_steps=3,
inner_batch_size=10,
meta_batch_size=5,
meta_learning_rate_initial=1,
meta_learning_rate_final=0,
num_meta_steps=3000)
experiment_logger = create_tensorboard_logger(context.name,
"framework_ours_test_1")
# Load and prepare Omniglot data
data_dir = REPO_ROOT / 'data' / 'omniglot'
omniglot_train_clients, omniglot_test_clients = load_omniglot_datasets(
str(data_dir.absolute()),
num_clients_train=num_clients_train,
num_clients_test=num_clients_test,
num_classes_per_client=num_classes_per_client,
num_shots_per_class=num_shots_per_class,
inner_batch_size=reptile_args.inner_batch_size,
random_seed=RANDOM_SEED)
# Prepare ModelArgs for task training
inner_optimizer_args = OptimizerArgs(
optimizer_class=reptile_args.inner_optimizer,
lr=reptile_args.inner_learning_rate,
betas=(0, 0.999))
inner_model_args = ModelArgs(reptile_args.model,
inner_optimizer_args,
num_classes=num_classes_per_client)
dummy_optimizer_args = OptimizerArgs(optimizer_class=optim.SGD)
meta_model_args = ModelArgs(reptile_args.model,
dummy_optimizer_args,
num_classes=num_classes_per_client)
####################
torch_model = OmniglotModel(num_classes=num_classes_per_client)
torch_optimizer = inner_optimizer_args.optimizer_class(
torch_model.parameters(), **inner_optimizer_args.optimizer_kwargs)
torch_criterion = torch.nn.CrossEntropyLoss()
####################
# Set up clients
# Since we are doing meta-learning, we need separate sets of training and
# test clients
train_clients = []
for c in omniglot_train_clients.train_data_local_dict.keys():
client = ReptileClient(
client_id=str(c),
model_args=inner_model_args,
context=context,
train_dataloader=omniglot_train_clients.train_data_local_dict[c],
num_train_samples=omniglot_train_clients.
data_local_train_num_dict[c],
test_dataloader=omniglot_train_clients.test_data_local_dict[c],
num_test_samples=omniglot_train_clients.
data_local_test_num_dict[c],
lightning_logger=experiment_logger)
#checkpoint_callback = ModelCheckpoint(
# filepath=str(client.get_checkpoint_path(suffix='cb').absolute()))
#client.set_trainer_callbacks([checkpoint_callback])
train_clients.append(client)
test_clients = []
for c in omniglot_test_clients.train_data_local_dict.keys():
client = ReptileClient(
client_id=str(c),
model_args=inner_model_args,
context=context,
train_dataloader=omniglot_test_clients.train_data_local_dict[c],
num_train_samples=omniglot_test_clients.
data_local_train_num_dict[c],
test_dataloader=omniglot_test_clients.test_data_local_dict[c],
num_test_samples=omniglot_test_clients.data_local_test_num_dict[c],
lightning_logger=experiment_logger)
test_clients.append(client)
# Set up server
server = ReptileServer(model_state=copy.deepcopy(torch_model.state_dict()),
participant_name='initial_server',
model_args=meta_model_args,
context=context,
initial_model_state=initial_model_state)
for i in range(reptile_args.num_meta_steps):
meta_batch = [
train_clients[k] for k in cyclerange(
i * reptile_args.meta_batch_size %
len(train_clients), (i + 1) * reptile_args.meta_batch_size %
len(train_clients), len(train_clients))
]
"""reptile.train_step(
meta_batch=meta_batch,
meta_step_size=cur_meta_step_size
)"""
reptile_train_step(
model=torch_model,
optimizer=torch_optimizer,
criterion=torch_criterion,
aggregator=server,
participants=meta_batch,
inner_training_args=reptile_args.get_inner_training_args(),
meta_training_args=reptile_args.get_meta_training_args(
frac_done=i / reptile_args.num_meta_steps))
if i % eval_iters == 0:
accuracies = []
# train-test set
# Pick one train client at random and test on it
client = [RANDOM.choice(train_clients)]
reptile_train_step(
model=torch_model,
optimizer=torch_optimizer,
criterion=torch_criterion,
aggregator=server,
participants=client,
inner_training_args=reptile_args.get_inner_training_args(
eval=True),
evaluation_mode=True)
inputs, labels = list(client[0]._test_dataloader)[0]
torch_model.load_state_dict(client[0].model_state)
test_preds = torch_model(inputs).argmax(dim=1)
num_correct = int(
sum([pred == label
for pred, label in zip(test_preds, labels)]))
##############result = evaluate_local_models(participants=client)
accuracies.append(num_correct / num_classes_per_client)
# log_loss_and_acc('global_model', result.get('test/loss'), result.get('test/acc'),
# experiment_logger, global_step=i+1)
#experiment_logger.experiment.add_scalar('train-test/acc/{}/mean'.format('global_model'),
# torch.mean(result.get('test/acc')),
# global_step=i + 1)
# test-test set
# Pick one test client at random and test on it
client = [RANDOM.choice(test_clients)]
reptile_train_step(
model=torch_model,
optimizer=torch_optimizer,
criterion=torch_criterion,
aggregator=server,
participants=client,
inner_training_args=reptile_args.get_inner_training_args(
eval=True),
evaluation_mode=True)
inputs, labels = list(client[0]._test_dataloader)[0]
torch_model.load_state_dict(client[0].model_state)
test_preds = torch_model(inputs).argmax(dim=1)
num_correct = int(
sum([pred == label
for pred, label in zip(test_preds, labels)]))
##############result = evaluate_local_models(participants=client)
accuracies.append(num_correct / num_classes_per_client)
# log_loss_and_acc('global_model', result.get('test/loss'), result.get('test/acc'),
# experiment_logger, global_step=i+1)
print('batch %d: train=%f test=%f' %
(i, accuracies[0], accuracies[1]))
experiment_logger.experiment.add_scalar(
'train-test/acc/{}/mean'.format('global_model'),
accuracies[0],
global_step=i)
experiment_logger.experiment.add_scalar(
'test-test/acc/{}/mean'.format('global_model'),
accuracies[1],
global_step=i)
"""
def run_reptile(context: ExperimentContext, initial_model_state=None):
num_clients_train = 10000
num_clients_test = 1000
num_classes_per_client = 5
num_shots_per_class = 5
# Every *eval_iters* meta steps, evaluation is performed on one random
# client in the training and test set, respectively
eval_iters = 10
reptile_args = ReptileTrainingArgs(model=OmniglotLightning,
inner_optimizer=optim.Adam,
inner_learning_rate=0.001,
num_inner_steps=5,
num_inner_steps_eval=50,
log_every_n_steps=3,
inner_batch_size=10,
meta_batch_size=5,
meta_learning_rate_initial=1,
meta_learning_rate_final=0,
num_meta_steps=3000)
experiment_logger = create_tensorboard_logger(context.name, (
f"nichol_reptile_our_dataloading;{num_clients_train}clients;{num_classes_per_client}"
f"-way{num_shots_per_class}-shot;"
f"mlr{str(reptile_args.meta_learning_rate_initial).replace('.', '')}"
f"ilr{str(reptile_args.inner_learning_rate).replace('.', '')}"
f"is{reptile_args.num_inner_steps}"))
# Load and prepare Omniglot data
data_dir = REPO_ROOT / 'data' / 'omniglot'
tf.disable_eager_execution()
omniglot_train_clients, omniglot_test_clients = load_omniglot_datasets(
str(data_dir.absolute()),
num_clients_train=num_clients_train,
num_clients_test=num_clients_test,
num_classes_per_client=num_classes_per_client,
num_shots_per_class=num_shots_per_class,
inner_batch_size=reptile_args.inner_batch_size,
tensorflow=True,
random_seed=RANDOM_SEED)
with tf.Session() as sess:
model = OmniglotModel(num_classes_per_client,
learning_rate=reptile_args.inner_learning_rate)
reptile = Reptile(sess, transductive=True, pre_step_op=weight_decay(1))
accuracy_ph = tf.placeholder(tf.float32, shape=())
tf.summary.scalar('accuracy', accuracy_ph)
merged = tf.summary.merge_all()
tf.global_variables_initializer().run()
sess.run(tf.global_variables_initializer())
for i in range(reptile_args.num_meta_steps):
frac_done = i / reptile_args.num_meta_steps
cur_meta_step_size = frac_done * reptile_args.meta_learning_rate_final + (
1 - frac_done) * reptile_args.meta_learning_rate_initial
meta_batch = {
k: omniglot_train_clients.train_data_local_dict[k]
for k in cyclerange(
i * reptile_args.meta_batch_size %
len(omniglot_train_clients.train_data_local_dict), (i +
1) *
reptile_args.meta_batch_size %
len(omniglot_train_clients.train_data_local_dict),
len(omniglot_train_clients.train_data_local_dict))
}
reptile.train_step(meta_batch=meta_batch,
input_ph=model.input_ph,
label_ph=model.label_ph,
minimize_op=model.minimize_op,
inner_iters=reptile_args.num_inner_steps,
meta_step_size=cur_meta_step_size)
if i % eval_iters == 0:
accuracies = []
k = RANDOM.randrange(
len(omniglot_train_clients.train_data_local_dict))
train_train = omniglot_train_clients.train_data_local_dict[k]
train_test = omniglot_train_clients.test_data_local_dict[k]
k = RANDOM.randrange(
len(omniglot_test_clients.train_data_local_dict))
test_train = omniglot_test_clients.train_data_local_dict[k]
test_test = omniglot_test_clients.test_data_local_dict[k]
for train_dl, test_dl in [(train_train, train_test),
(test_train, test_test)]:
correct = reptile.evaluate(
train_data_loader=train_dl,
test_data_loader=test_dl,
input_ph=model.input_ph,
label_ph=model.label_ph,
minimize_op=model.minimize_op,
predictions=model.predictions,
inner_iters=reptile_args.num_inner_steps_eval)
#summary = sess.run(merged, feed_dict={accuracy_ph: correct/num_classes_per_client})
accuracies.append(correct / num_classes_per_client)
print('batch %d: train=%f test=%f' %
(i, accuracies[0], accuracies[1]))
# Write to TensorBoard
experiment_logger.experiment.add_scalar(
'train-test/acc/{}/mean'.format('global_model'),
accuracies[0],
global_step=i)
experiment_logger.experiment.add_scalar(
'test-test/acc/{}/mean'.format('global_model'),
accuracies[1],
global_step=i)
def run_reptile(context: str, initial_model_state=None):
args = argument_parser().parse_args()
RANDOM = random.Random(args.seed)
experiment_logger = create_tensorboard_logger(
'reptile', (f"{context};seed{args.seed};"
f"train-clients{args.train_clients};"
f"{args.classes}-way{args.shots}-shot;"
f"ib{args.inner_batch}ii{args.inner_iters}"
f"ilr{str(args.learning_rate).replace('.', '')}"
f"ms{str(args.meta_step).replace('.', '')}"
f"mb{args.meta_batch}ei{args.eval_iters}"
f"{'sgd' if args.sgd else 'adam'}"))
# Load and prepare Omniglot data
data_dir = REPO_ROOT / 'data' / 'omniglot'
tf.disable_eager_execution()
omniglot_train_clients, omniglot_test_clients = load_omniglot_datasets(
str(data_dir.absolute()),
num_clients_train=args.train_clients,
num_clients_test=args.test_clients,
num_classes_per_client=args.classes,
num_shots_per_class=args.shots,
inner_batch_size=args.inner_batch,
tensorflow=True,
random_seed=args.seed)
model_kwargs = {'learning_rate': args.learning_rate}
if args.sgd:
model_kwargs['optimizer'] = tf.train.GradientDescentOptimizer
model = OmniglotModel(num_classes=args.classes, **model_kwargs)
with tf.Session() as sess:
reptile = ReptileForFederatedData(session=sess,
transductive=True,
pre_step_op=weight_decay(1))
accuracy_ph = tf.placeholder(tf.float32, shape=())
tf.summary.scalar('accuracy', accuracy_ph)
merged = tf.summary.merge_all()
tf.global_variables_initializer().run()
sess.run(tf.global_variables_initializer())
for i in range(args.meta_iters):
frac_done = i / args.meta_iters
cur_meta_step_size = frac_done * args.meta_step_final + (
1 - frac_done) * args.meta_step
crange = cyclerange(
start=i * args.meta_batch %
len(omniglot_train_clients.train_data_local_dict),
stop=(i + 1) * args.meta_batch %
len(omniglot_train_clients.train_data_local_dict),
len=len(omniglot_train_clients.train_data_local_dict))
#print(f"Meta-step {i}: train clients {crange[0]}-{crange[-1]}")
meta_batch = {
k: omniglot_train_clients.train_data_local_dict[k]
for k in crange
}
reptile.train_step(meta_batch=meta_batch,
input_ph=model.input_ph,
label_ph=model.label_ph,
minimize_op=model.minimize_op,
inner_iters=args.inner_iters,
meta_step_size=cur_meta_step_size)
if i % args.eval_interval == 0:
accuracies = []
k = RANDOM.randrange(
len(omniglot_train_clients.train_data_local_dict))
train_train = omniglot_train_clients.train_data_local_dict[k]
train_test = omniglot_train_clients.test_data_local_dict[k]
k = RANDOM.randrange(
len(omniglot_test_clients.train_data_local_dict))
test_train = omniglot_test_clients.train_data_local_dict[k]
test_test = omniglot_test_clients.test_data_local_dict[k]
for train_dl, test_dl in [(train_train, train_test),
(test_train, test_test)]:
correct = reptile.evaluate(train_data_loader=train_dl,
test_data_loader=test_dl,
input_ph=model.input_ph,
label_ph=model.label_ph,
minimize_op=model.minimize_op,
predictions=model.predictions,
inner_iters=args.eval_iters)
#summary = sess.run(merged, feed_dict={accuracy_ph: correct/num_classes_per_client})
accuracies.append(correct / args.classes)
print('batch %d: train=%f test=%f' %
(i, accuracies[0], accuracies[1]))
# Write to TensorBoard
experiment_logger.experiment.add_scalar(
'train-test/acc/{}/mean'.format('global_model'),
accuracies[0],
global_step=i)
experiment_logger.experiment.add_scalar(
'test-test/acc/{}/mean'.format('global_model'),
accuracies[1],
global_step=i)
# Final evaluation on a sample of training/test clients
for label, dataset in zip(
['Train', 'Test'],
[omniglot_train_clients, omniglot_test_clients]):
keys = RANDOM.sample(dataset.train_data_local_dict.keys(),
args.eval_samples)
train_eval_sample = {
k: dataset.train_data_local_dict[k]
for k in keys
}
test_eval_sample = {
k: dataset.test_data_local_dict[k]
for k in keys
}
accuracy = evaluate(sess=sess,
model=model,
train_dataloaders=train_eval_sample,
test_dataloaders=test_eval_sample,
num_classes=args.classes,
eval_inner_iters=args.eval_iters,
transductive=True)
experiment_logger.experiment.add_scalar(f'final_{label}_acc',
accuracy,
global_step=0)
print(f"{label} accuracy: {accuracy}")