def main(args):
"""
Last Avalanche version reference performance (online = 1 epoch):
Class-incremental (online):
Top1_Acc_Stream/eval_phase/test_stream = 0.9421
Data-incremental (online:
Top1_Acc_Stream/eval_phase/test_stream = 0.9309
These are reference results for a single run.
"""
# --- DEFAULT PARAMS ONLINE DATA INCREMENTAL LEARNING
nb_tasks = 5 # Can still design the data stream based on tasks
batch_size = 10 # Learning agent only has small amount of data available
epochs = 1 # How many times to process each mini-batch
return_task_id = False # Data incremental (task-agnostic/task-free)
# --- CONFIG
device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available()
and args.cuda >= 0 else "cpu")
# ---------
# --- SCENARIO CREATION
n_classes = 10
task_scenario = SplitMNIST(
nb_tasks,
return_task_id=return_task_id,
fixed_class_order=[i for i in range(n_classes)],
)
# Make data incremental (one batch = one experience)
scenario = data_incremental_benchmark(task_scenario,
experience_size=batch_size)
print(
f"{scenario.n_experiences} batches in online data incremental setup.")
# 6002 batches for SplitMNIST with batch size 10
# ---------
# MODEL CREATION
model = SimpleMLP(num_classes=args.featsize,
hidden_size=400,
hidden_layers=2,
drop_rate=0)
# choose some metrics and evaluation method
logger = TextLogger()
eval_plugin = EvaluationPlugin(
accuracy_metrics(experience=True, stream=True),
loss_metrics(experience=False, stream=True),
StreamForgetting(),
loggers=[logger],
benchmark=scenario,
)
# CoPE PLUGIN
cope = CoPEPlugin(mem_size=2000,
alpha=0.99,
p_size=args.featsize,
n_classes=n_classes)
# CREATE THE STRATEGY INSTANCE (NAIVE) WITH CoPE PLUGIN
cl_strategy = Naive(
model,
torch.optim.SGD(model.parameters(), lr=0.01),
cope.ppp_loss, # CoPE PPP-Loss
train_mb_size=batch_size,
train_epochs=epochs,
eval_mb_size=100,
device=device,
plugins=[cope],
evaluator=eval_plugin,
)
# TRAINING LOOP
print("Starting experiment...")
results = []
cl_strategy.train(scenario.train_stream)
print("Computing accuracy on the whole test set")
results.append(cl_strategy.eval(scenario.test_stream))
def setUpClass(cls) -> None:
torch.manual_seed(0)
np.random.seed(0)
random.seed(0)
n_samples_per_class = 100
datasets = []
for i in range(3):
dataset = make_classification(n_samples=3 * n_samples_per_class,
n_classes=3,
n_features=3,
n_informative=3,
n_redundant=0)
X = torch.from_numpy(dataset[0]).float()
y = torch.from_numpy(dataset[1]).long()
train_X, test_X, train_y, test_y = train_test_split(X,
y,
train_size=0.5,
shuffle=True,
stratify=y)
datasets.append((train_X, train_y, test_X, test_y))
tr_ds = [
AvalancheTensorDataset(
tr_X,
tr_y,
dataset_type=AvalancheDatasetType.CLASSIFICATION,
task_labels=torch.randint(0, 3, (150, )).tolist())
for tr_X, tr_y, _, _ in datasets
]
ts_ds = [
AvalancheTensorDataset(
ts_X,
ts_y,
dataset_type=AvalancheDatasetType.CLASSIFICATION,
task_labels=torch.randint(0, 3, (150, )).tolist())
for _, _, ts_X, ts_y in datasets
]
benchmark = dataset_benchmark(train_datasets=tr_ds,
test_datasets=ts_ds)
model = SimpleMLP(num_classes=3, input_size=3)
f = open('log.txt', 'w')
text_logger = TextLogger(f)
eval_plugin = EvaluationPlugin(
accuracy_metrics(minibatch=True,
epoch=True,
epoch_running=True,
experience=True,
stream=True),
loss_metrics(minibatch=True,
epoch=True,
epoch_running=True,
experience=True,
stream=True),
forgetting_metrics(experience=True, stream=True, task=True),
confusion_matrix_metrics(num_classes=3,
save_image=False,
normalize='all',
stream=True),
bwt_metrics(experience=True, stream=True, task=True),
cpu_usage_metrics(minibatch=True,
epoch=True,
epoch_running=True,
experience=True,
stream=True),
timing_metrics(minibatch=True,
epoch=True,
epoch_running=True,
experience=True,
stream=True),
ram_usage_metrics(every=0.5,
minibatch=True,
epoch=True,
experience=True,
stream=True),
disk_usage_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
MAC_metrics(minibatch=True, epoch=True, experience=True),
loggers=[text_logger],
collect_all=True) # collect all metrics (set to True by default)
cl_strategy = BaseStrategy(model,
SGD(model.parameters(),
lr=0.001,
momentum=0.9),
CrossEntropyLoss(),
train_mb_size=2,
train_epochs=2,
eval_mb_size=2,
device=DEVICE,
evaluator=eval_plugin,
eval_every=1)
for i, experience in enumerate(benchmark.train_stream):
cl_strategy.train(experience,
eval_streams=[benchmark.test_stream[i]],
shuffle=False)
cl_strategy.eval(benchmark.test_stream)
cls.all_metrics = cl_strategy.evaluator.get_all_metrics()
f.close()
# with open(os.path.join(pathlib.Path(__file__).parent.absolute(),
# 'target_metrics',
# 'tpp.pickle'), 'wb') as f:
# pickle.dump(dict(cls.all_metrics), f,
# protocol=pickle.HIGHEST_PROTOCOL)
with open(
os.path.join(
pathlib.Path(__file__).parent.absolute(), 'target_metrics',
'tpp.pickle'), 'rb') as f:
cls.ref = pickle.load(f)
def main(args):
model = SimpleMLP(hidden_size=args.hs)
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
criterion = torch.nn.CrossEntropyLoss()
# check if selected GPU is available or use CPU
assert args.cuda == -1 or args.cuda >= 0, "cuda must be -1 or >= 0."
device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available()
and args.cuda >= 0 else "cpu")
print(f"Using device: {device}")
# create scenario
if args.scenario == "pmnist":
scenario = PermutedMNIST(n_experiences=args.permutations)
elif args.scenario == "smnist":
mnist_train = MNIST(
root=expanduser("~") + "/.avalanche/data/mnist/",
train=True,
download=True,
transform=ToTensor(),
)
mnist_test = MNIST(
root=expanduser("~") + "/.avalanche/data/mnist/",
train=False,
download=True,
transform=ToTensor(),
)
scenario = nc_benchmark(mnist_train,
mnist_test,
5,
task_labels=False,
seed=1234)
else:
raise ValueError("Wrong scenario name. Allowed pmnist, smnist.")
# choose some metrics and evaluation method
interactive_logger = InteractiveLogger()
tensorboard_logger = TensorboardLogger()
eval_plugin = EvaluationPlugin(
accuracy_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
loss_metrics(minibatch=True, epoch=True, experience=True, stream=True),
forgetting_metrics(experience=True, stream=True),
bwt_metrics(experience=True, stream=True),
loggers=[interactive_logger, tensorboard_logger],
)
# create strategy
strategy = EWC(
model,
optimizer,
criterion,
args.ewc_lambda,
args.ewc_mode,
decay_factor=args.decay_factor,
train_epochs=args.epochs,
device=device,
train_mb_size=args.minibatch_size,
evaluator=eval_plugin,
)
# train on the selected scenario with the chosen strategy
print("Starting experiment...")
results = []
for experience in scenario.train_stream:
print("Start training on experience ", experience.current_experience)
strategy.train(experience)
print("End training on experience", experience.current_experience)
print("Computing accuracy on the test set")
results.append(strategy.eval(scenario.test_stream[:]))
def main(args):
# --- CONFIG
device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available()
and args.cuda >= 0 else "cpu")
# ---------
# --- TRANSFORMATIONS
train_transform = transforms.Compose([
RandomCrop(28, padding=4),
ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
test_transform = transforms.Compose(
[ToTensor(), transforms.Normalize((0.1307, ), (0.3081, ))])
# ---------
# --- SCENARIO CREATION
mnist_train = MNIST(root=expanduser("~") + "/.avalanche/data/mnist/",
train=True,
download=True,
transform=train_transform)
mnist_test = MNIST(root=expanduser("~") + "/.avalanche/data/mnist/",
train=False,
download=True,
transform=test_transform)
scenario = nc_benchmark(mnist_train,
mnist_test,
5,
task_labels=False,
seed=1234)
# ---------
# MODEL CREATION
model = SimpleMLP(num_classes=scenario.n_classes)
# DEFINE THE EVALUATION PLUGIN AND LOGGER
# The evaluation plugin manages the metrics computation.
# It takes as argument a list of metrics and a list of loggers.
# The evaluation plugin calls the loggers to serialize the metrics
# and save them in persistent memory or print them in the standard output.
# log to text file
text_logger = TextLogger(open('log.txt', 'a'))
# print to stdout
interactive_logger = InteractiveLogger()
csv_logger = CSVLogger()
eval_plugin = EvaluationPlugin(
accuracy_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
loss_metrics(minibatch=True, epoch=True, experience=True, stream=True),
forgetting_metrics(experience=True, stream=True),
cpu_usage_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
timing_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
ram_usage_metrics(every=0.5,
minibatch=True,
epoch=True,
experience=True,
stream=True),
gpu_usage_metrics(args.cuda,
every=0.5,
minibatch=True,
epoch=True,
experience=True,
stream=True),
disk_usage_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
MAC_metrics(minibatch=True, epoch=True, experience=True),
loggers=[interactive_logger, text_logger, csv_logger],
collect_all=True) # collect all metrics (set to True by default)
# CREATE THE STRATEGY INSTANCE (NAIVE)
cl_strategy = Naive(model,
SGD(model.parameters(), lr=0.001, momentum=0.9),
CrossEntropyLoss(),
train_mb_size=500,
train_epochs=1,
eval_mb_size=100,
device=device,
evaluator=eval_plugin,
eval_every=1)
# TRAINING LOOP
print('Starting experiment...')
results = []
for i, experience in enumerate(scenario.train_stream):
print("Start of experience: ", experience.current_experience)
print("Current Classes: ", experience.classes_in_this_experience)
# train returns a dictionary containing last recorded value
# for each metric.
res = cl_strategy.train(experience,
eval_streams=[scenario.test_stream[i]])
print('Training completed')
print('Computing accuracy on the whole test set')
# test returns a dictionary with the last metric collected during
# evaluation on that stream
results.append(cl_strategy.eval(scenario.test_stream))
print(f"Test metrics:\n{results}")
# Dict with all the metric curves,
# only available when `collect_all` is True.
# Each entry is a (x, metric value) tuple.
# You can use this dictionary to manipulate the
# metrics without avalanche.
all_metrics = cl_strategy.evaluator.get_all_metrics()
print(f"Stored metrics: {list(all_metrics.keys())}")
def main(args):
# --- CONFIG
device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available()
and args.cuda >= 0 else "cpu")
# ---------
# --- TRANSFORMATIONS
train_transform = transforms.Compose([
RandomCrop(28, padding=4),
ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
test_transform = transforms.Compose(
[ToTensor(), transforms.Normalize((0.1307, ), (0.3081, ))])
# ---------
# --- SCENARIO CREATION
mnist_train = MNIST('./data/mnist',
train=True,
download=True,
transform=train_transform)
mnist_test = MNIST('./data/mnist',
train=False,
download=True,
transform=test_transform)
scenario = nc_scenario(mnist_train,
mnist_test,
5,
task_labels=False,
seed=1234)
# ---------
# MODEL CREATION
model = SimpleMLP(num_classes=scenario.n_classes)
# DEFINE THE EVALUATION PLUGIN AND LOGGER
# The evaluation plugin manages the metrics computation.
# It takes as argument a list of metrics and a list of loggers.
# The evaluation plugin calls the loggers to serialize the metrics
# and save them in persistent memory or print them in the standard output.
# log to text file
text_logger = TextLogger(open('log.txt', 'a'))
# print to stdout
interactive_logger = InteractiveLogger()
eval_plugin = EvaluationPlugin(accuracy_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
loss_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
cpu_usage_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
timing_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
ExperienceForgetting(),
loggers=[interactive_logger, text_logger])
# CREATE THE STRATEGY INSTANCE (NAIVE)
cl_strategy = Naive(model,
SGD(model.parameters(), lr=0.001, momentum=0.9),
CrossEntropyLoss(),
train_mb_size=500,
train_epochs=1,
eval_mb_size=100,
device=device,
evaluator=eval_plugin)
# TRAINING LOOP
print('Starting experiment...')
results = []
for experience in scenario.train_stream:
print("Start of experience: ", experience.current_experience)
print("Current Classes: ", experience.classes_in_this_experience)
# train returns a list of dictionaries (one for each experience). Each
# dictionary stores the last value of each metric curve emitted
# during training.
res = cl_strategy.train(experience)
print('Training completed')
print('Computing accuracy on the whole test set')
# test also returns a dictionary
results.append(cl_strategy.eval(scenario.test_stream))
print(f"Test metrics:\n{results}")
# All the metric curves (x,y values) are stored inside the evaluator
# (can be disabled). You can use this dictionary to manipulate the
# metrics without avalanche.
all_metrics = cl_strategy.evaluator.all_metrics
print(f"Stored metrics: {list(all_metrics.keys())}")
mname = 'Top1_Acc_Task/Task000'
print(f"{mname}: {cl_strategy.evaluator.all_metrics[mname]}")
def main(args):
# --- CONFIG
device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available()
and args.cuda >= 0 else "cpu")
# ---------
tr_ds = [
AvalancheTensorDataset(
torch.randn(10, 3),
torch.randint(0, 3, (10, )).tolist(),
task_labels=torch.randint(0, 5, (10, )).tolist(),
) for _ in range(3)
]
ts_ds = [
AvalancheTensorDataset(
torch.randn(10, 3),
torch.randint(0, 3, (10, )).tolist(),
task_labels=torch.randint(0, 5, (10, )).tolist(),
) for _ in range(3)
]
scenario = create_multi_dataset_generic_benchmark(train_datasets=tr_ds,
test_datasets=ts_ds)
# ---------
# MODEL CREATION
model = SimpleMLP(num_classes=3, input_size=3)
# DEFINE THE EVALUATION PLUGIN AND LOGGER
# The evaluation plugin manages the metrics computation.
# It takes as argument a list of metrics and a list of loggers.
# The evaluation plugin calls the loggers to serialize the metrics
# and save them in persistent memory or print them in the standard output.
# log to text file
text_logger = TextLogger(open("log.txt", "a"))
# print to stdout
interactive_logger = InteractiveLogger()
csv_logger = CSVLogger()
eval_plugin = EvaluationPlugin(
accuracy_metrics(
minibatch=True,
epoch=True,
epoch_running=True,
experience=True,
stream=True,
),
loss_metrics(
minibatch=True,
epoch=True,
epoch_running=True,
experience=True,
stream=True,
),
forgetting_metrics(experience=True, stream=True),
bwt_metrics(experience=True, stream=True),
cpu_usage_metrics(
minibatch=True,
epoch=True,
epoch_running=True,
experience=True,
stream=True,
),
timing_metrics(
minibatch=True,
epoch=True,
epoch_running=True,
experience=True,
stream=True,
),
ram_usage_metrics(every=0.5,
minibatch=True,
epoch=True,
experience=True,
stream=True),
gpu_usage_metrics(
args.cuda,
every=0.5,
minibatch=True,
epoch=True,
experience=True,
stream=True,
),
disk_usage_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
MAC_metrics(minibatch=True, epoch=True, experience=True),
loggers=[interactive_logger, text_logger, csv_logger],
collect_all=True,
) # collect all metrics (set to True by default)
# CREATE THE STRATEGY INSTANCE (NAIVE)
cl_strategy = Naive(
model,
SGD(model.parameters(), lr=0.001, momentum=0.9),
CrossEntropyLoss(),
train_mb_size=500,
train_epochs=1,
eval_mb_size=100,
device=device,
evaluator=eval_plugin,
eval_every=1,
)
# TRAINING LOOP
print("Starting experiment...")
results = []
for i, experience in enumerate(scenario.train_stream):
print("Start of experience: ", experience.current_experience)
print("Current Classes: ", experience.classes_in_this_experience)
# train returns a dictionary containing last recorded value
# for each metric.
res = cl_strategy.train(experience,
eval_streams=[scenario.test_stream])
print("Training completed")
print("Computing accuracy on the whole test set")
# test returns a dictionary with the last metric collected during
# evaluation on that stream
results.append(cl_strategy.eval(scenario.test_stream))
print(f"Test metrics:\n{results}")
# Dict with all the metric curves,
# only available when `collect_all` is True.
# Each entry is a (x, metric value) tuple.
# You can use this dictionary to manipulate the
# metrics without avalanche.
all_metrics = cl_strategy.evaluator.get_all_metrics()
print(f"Stored metrics: {list(all_metrics.keys())}")
def main(args):
# --- CONFIG
device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available()
and args.cuda >= 0 else "cpu")
# ---------
# --- TRANSFORMATIONS
train_transform = transforms.Compose([
RandomCrop(28, padding=4),
ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, )),
])
test_transform = transforms.Compose(
[ToTensor(), transforms.Normalize((0.1307, ), (0.3081, ))])
# ---------
# --- SCENARIO CREATION
mnist_train = MNIST(
root=expanduser("~") + "/.avalanche/data/mnist/",
train=True,
download=True,
transform=train_transform,
)
mnist_test = MNIST(
root=expanduser("~") + "/.avalanche/data/mnist/",
train=False,
download=True,
transform=test_transform,
)
scenario = nc_benchmark(mnist_train,
mnist_test,
5,
task_labels=False,
seed=1234)
# ---------
# MODEL CREATION
model = SimpleMLP(num_classes=scenario.n_classes)
# CREATE THE STRATEGY INSTANCE (NAIVE)
cl_strategy = Naive(
model,
SGD(model.parameters(), lr=0.001, momentum=0.9),
CrossEntropyLoss(),
train_mb_size=100,
train_epochs=4,
eval_mb_size=100,
device=device,
)
# TRAINING LOOP
print("Starting experiment...")
results = []
for experience in scenario.train_stream:
print("Start of experience: ", experience.current_experience)
print("Current Classes: ", experience.classes_in_this_experience)
cl_strategy.train(experience)
print("Training completed")
print("Computing accuracy on the whole test set")
results.append(cl_strategy.eval(scenario.test_stream))
class MyCumulativeStrategy(Cumulative):
def make_train_dataloader(self, shuffle=True, **kwargs):
# you can override make_train_dataloader to change the
# strategy's dataloader
# remember to iterate over self.adapted_dataset
self.dataloader = TaskBalancedDataLoader(
self.adapted_dataset, batch_size=self.train_mb_size
)
if __name__ == "__main__":
benchmark = SplitMNIST(n_experiences=5)
model = SimpleMLP(input_size=784, hidden_size=10)
opt = SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=0.001)
# we use our custom strategy to change the dataloading policy.
cl_strategy = MyCumulativeStrategy(
model,
opt,
CrossEntropyLoss(),
train_epochs=1,
train_mb_size=512,
eval_mb_size=512,
)
for step in benchmark.train_stream:
cl_strategy.train(step)
cl_strategy.eval(step)
def setUpClass(cls) -> None:
torch.manual_seed(0)
np.random.seed(0)
random.seed(0)
n_samples_per_class = 100
datasets = []
for i in range(3):
dataset = make_classification(
n_samples=3 * n_samples_per_class,
n_classes=3,
n_features=3,
n_informative=3,
n_redundant=0,
)
X = torch.from_numpy(dataset[0]).float()
y = torch.from_numpy(dataset[1]).long()
train_X, test_X, train_y, test_y = train_test_split(X,
y,
train_size=0.5,
shuffle=True,
stratify=y)
datasets.append((train_X, train_y, test_X, test_y))
tr_ds = [
AvalancheTensorDataset(
tr_X,
tr_y,
dataset_type=AvalancheDatasetType.CLASSIFICATION,
task_labels=torch.randint(0, 3, (150, )).tolist(),
) for tr_X, tr_y, _, _ in datasets
]
ts_ds = [
AvalancheTensorDataset(
ts_X,
ts_y,
dataset_type=AvalancheDatasetType.CLASSIFICATION,
task_labels=torch.randint(0, 3, (150, )).tolist(),
) for _, _, ts_X, ts_y in datasets
]
benchmark = dataset_benchmark(train_datasets=tr_ds,
test_datasets=ts_ds)
model = SimpleMLP(num_classes=3, input_size=3)
f = open("log.txt", "w")
text_logger = TextLogger(f)
eval_plugin = EvaluationPlugin(
accuracy_metrics(
minibatch=True,
epoch=True,
epoch_running=True,
experience=True,
stream=True,
trained_experience=True,
),
loss_metrics(
minibatch=True,
epoch=True,
epoch_running=True,
experience=True,
stream=True,
),
forgetting_metrics(experience=True, stream=True),
confusion_matrix_metrics(num_classes=3,
save_image=False,
normalize="all",
stream=True),
bwt_metrics(experience=True, stream=True),
forward_transfer_metrics(experience=True, stream=True),
cpu_usage_metrics(
minibatch=True,
epoch=True,
epoch_running=True,
experience=True,
stream=True,
),
timing_metrics(
minibatch=True,
epoch=True,
epoch_running=True,
experience=True,
stream=True,
),
ram_usage_metrics(
every=0.5,
minibatch=True,
epoch=True,
experience=True,
stream=True,
),
disk_usage_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
MAC_metrics(minibatch=True, epoch=True, experience=True),
loggers=[text_logger],
collect_all=True,
) # collect all metrics (set to True by default)
cl_strategy = BaseStrategy(
model,
SGD(model.parameters(), lr=0.001, momentum=0.9),
CrossEntropyLoss(),
train_mb_size=4,
train_epochs=2,
eval_mb_size=2,
device=DEVICE,
evaluator=eval_plugin,
eval_every=1,
)
for i, experience in enumerate(benchmark.train_stream):
cl_strategy.train(experience,
eval_streams=[benchmark.test_stream],
shuffle=False)
cl_strategy.eval(benchmark.test_stream)
cls.all_metrics = cl_strategy.evaluator.get_all_metrics()
f.close()
# Set the environment variable UPDATE_METRICS to True to update
# the pickle file with target values.
# Make sure the old tests were passing for all unchanged metrics
if UPDATE_METRICS:
with open(
os.path.join(
pathlib.Path(__file__).parent.absolute(),
"target_metrics",
"tpp.pickle",
),
"wb",
) as f:
pickle.dump(dict(cls.all_metrics), f, protocol=4)
with open(
os.path.join(
pathlib.Path(__file__).parent.absolute(),
"target_metrics",
"tpp.pickle",
),
"rb",
) as f:
cls.ref = pickle.load(f)
def main(args):
model = SimpleMLP(hidden_size=args.hs)
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
criterion = torch.nn.CrossEntropyLoss()
# check if selected GPU is available or use CPU
assert args.cuda == -1 or args.cuda >= 0, "cuda must be -1 or >= 0."
device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available()
and args.cuda >= 0 else "cpu")
print(f'Using device: {device}')
# create scenario
if args.scenario == 'pmnist':
scenario = PermutedMNIST(n_experiences=args.permutations)
elif args.scenario == 'smnist':
scenario = SplitMNIST(n_experiences=5, return_task_id=False)
else:
raise ValueError("Wrong scenario name. Allowed pmnist, smnist.")
# choose some metrics and evaluation method
interactive_logger = InteractiveLogger()
eval_plugin = EvaluationPlugin(accuracy_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
loss_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
ExperienceForgetting(),
loggers=[interactive_logger])
# create strategy
if args.strategy == 'gem':
strategy = GEM(model,
optimizer,
criterion,
args.patterns_per_exp,
args.memory_strength,
train_epochs=args.epochs,
device=device,
train_mb_size=10,
evaluator=eval_plugin)
elif args.strategy == 'agem':
strategy = AGEM(model,
optimizer,
criterion,
args.patterns_per_exp,
args.sample_size,
train_epochs=args.epochs,
device=device,
train_mb_size=10,
evaluator=eval_plugin)
else:
raise ValueError("Wrong strategy name. Allowed gem, agem.")
# train on the selected scenario with the chosen strategy
print('Starting experiment...')
results = []
for experience in scenario.train_stream:
print("Start training on experience ", experience.current_experience)
strategy.train(experience)
print("End training on experience ", experience.current_experience)
print('Computing accuracy on the test set')
results.append(strategy.eval(scenario.test_stream[:]))
def main(args):
# --- CONFIG
device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available()
and args.cuda >= 0 else "cpu")
# ---------
# --- TRANSFORMATIONS
train_transform = transforms.Compose([
RandomCrop(28, padding=4),
ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
test_transform = transforms.Compose(
[ToTensor(), transforms.Normalize((0.1307, ), (0.3081, ))])
# ---------
# --- SCENARIO CREATION
mnist_train = MNIST('./data/mnist',
train=True,
download=True,
transform=train_transform)
mnist_test = MNIST('./data/mnist',
train=False,
download=True,
transform=test_transform)
scenario = nc_scenario(mnist_train,
mnist_test,
5,
task_labels=False,
seed=1234)
# ---------
# MODEL CREATION
model = SimpleMLP(num_classes=scenario.n_classes)
eval_plugin = EvaluationPlugin(
accuracy_metrics(epoch=True, experience=True, stream=True),
loss_metrics(epoch=True, experience=True, stream=True),
# save image should be False to appropriately view
# results in Interactive Logger.
# a tensor will be printed
StreamConfusionMatrix(save_image=False, normalize='all'),
loggers=InteractiveLogger())
# CREATE THE STRATEGY INSTANCE (NAIVE)
cl_strategy = Naive(model,
SGD(model.parameters(), lr=0.001, momentum=0.9),
CrossEntropyLoss(),
train_mb_size=100,
train_epochs=4,
eval_mb_size=100,
device=device,
evaluator=eval_plugin,
plugins=[ReplayPlugin(5000)])
# TRAINING LOOP
print('Starting experiment...')
results = []
for experience in scenario.train_stream:
print("Start of experience: ", experience.current_experience)
print("Current Classes: ", experience.classes_in_this_experience)
cl_strategy.train(experience)
print('Training completed')
print('Computing accuracy on the whole test set')
results.append(cl_strategy.eval(scenario.test_stream))
def test_incremental_classifier(self):
model = SimpleMLP(input_size=6, hidden_size=10)
model.classifier = IncrementalClassifier(in_features=10)
optimizer = SGD(model.parameters(), lr=1e-3)
criterion = CrossEntropyLoss()
benchmark = self.benchmark
strategy = Naive(
model,
optimizer,
criterion,
train_mb_size=100,
train_epochs=1,
eval_mb_size=100,
device="cpu",
)
strategy.evaluator.loggers = [TextLogger(sys.stdout)]
print(
"Current Classes: ",
benchmark.train_stream[0].classes_in_this_experience,
)
print(
"Current Classes: ",
benchmark.train_stream[4].classes_in_this_experience,
)
# train on first task
strategy.train(benchmark.train_stream[0])
w_ptr = model.classifier.classifier.weight.data_ptr()
b_ptr = model.classifier.classifier.bias.data_ptr()
opt_params_ptrs = [
w.data_ptr() for group in optimizer.param_groups
for w in group["params"]
]
# classifier params should be optimized
assert w_ptr in opt_params_ptrs
assert b_ptr in opt_params_ptrs
# train again on the same task.
strategy.train(benchmark.train_stream[0])
# parameters should not change.
assert w_ptr == model.classifier.classifier.weight.data_ptr()
assert b_ptr == model.classifier.classifier.bias.data_ptr()
# the same classifier params should still be optimized
assert w_ptr in opt_params_ptrs
assert b_ptr in opt_params_ptrs
# update classifier with new classes.
old_w_ptr, old_b_ptr = w_ptr, b_ptr
strategy.train(benchmark.train_stream[4])
opt_params_ptrs = [
w.data_ptr() for group in optimizer.param_groups
for w in group["params"]
]
new_w_ptr = model.classifier.classifier.weight.data_ptr()
new_b_ptr = model.classifier.classifier.bias.data_ptr()
# weights should change.
assert old_w_ptr != new_w_ptr
assert old_b_ptr != new_b_ptr
# Old params should not be optimized. New params should be optimized.
assert old_w_ptr not in opt_params_ptrs
assert old_b_ptr not in opt_params_ptrs
assert new_w_ptr in opt_params_ptrs
assert new_b_ptr in opt_params_ptrs
accuracy_metrics(minibatch=True, epoch=True,
experience=True, stream=True),
loss_metrics(minibatch=True, epoch=True, experience=True, stream=True),
timing_metrics(epoch=True, epoch_running=True),
ExperienceForgetting(),
cpu_usage_metrics(experience=True),
StreamConfusionMatrix(num_classes=2, save_image=False),
disk_usage_metrics(minibatch=True, epoch=True,
experience=True, stream=True),
loggers=[interactive_logger, text_logger, tb_logger],
)
if arch == "GEM":
cl_strategy = GEM(
model,
optimizer=Adam(model.parameters()),
patterns_per_exp=4400,
criterion=CrossEntropyLoss(),
train_mb_size=128,
train_epochs=50,
eval_mb_size=128,
evaluator=eval_plugin,
device=device,
)
else:
cl_strategy = EWC(
model,
optimizer=Adam(model.parameters()),
ewc_lambda=0.001,
criterion=CrossEntropyLoss(),
train_mb_size=128,
def main(args):
# --- CONFIG
device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available()
and args.cuda >= 0 else "cpu")
n_batches = 5
# ---------
# --- TRANSFORMATIONS
train_transform = transforms.Compose([
RandomCrop(28, padding=4),
ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, )),
])
test_transform = transforms.Compose(
[ToTensor(), transforms.Normalize((0.1307, ), (0.3081, ))])
# ---------
# --- SCENARIO CREATION
mnist_train = MNIST(
root=expanduser("~") + "/.avalanche/data/mnist/",
train=True,
download=True,
transform=train_transform,
)
mnist_test = MNIST(
root=expanduser("~") + "/.avalanche/data/mnist/",
train=False,
download=True,
transform=test_transform,
)
scenario = nc_benchmark(mnist_train,
mnist_test,
n_batches,
task_labels=False,
seed=1234)
# ---------
# MODEL CREATION
model = SimpleMLP(num_classes=scenario.n_classes)
# choose some metrics and evaluation method
interactive_logger = InteractiveLogger()
eval_plugin = EvaluationPlugin(
accuracy_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
loss_metrics(minibatch=True, epoch=True, experience=True, stream=True),
forgetting_metrics(experience=True),
loggers=[interactive_logger],
)
# CREATE THE STRATEGY INSTANCE (NAIVE)
cl_strategy = Naive(
model,
torch.optim.Adam(model.parameters(), lr=0.001),
CrossEntropyLoss(),
train_mb_size=100,
train_epochs=4,
eval_mb_size=100,
device=device,
plugins=[ReplayPlugin(mem_size=10000)],
evaluator=eval_plugin,
)
# TRAINING LOOP
print("Starting experiment...")
results = []
for experience in scenario.train_stream:
print("Start of experience ", experience.current_experience)
cl_strategy.train(experience)
print("Training completed")
print("Computing accuracy on the whole test set")
results.append(cl_strategy.eval(scenario.test_stream))
def test_periodic_eval(self):
model = SimpleMLP(input_size=6, hidden_size=10)
model.classifier = IncrementalClassifier(model.classifier.in_features)
benchmark = get_fast_benchmark()
optimizer = SGD(model.parameters(), lr=1e-3)
criterion = CrossEntropyLoss()
curve_key = "Top1_Acc_Stream/eval_phase/train_stream/Task000"
###################
# Case #1: No eval
###################
# we use stream acc. because it emits a single value
# for each eval loop.
acc = StreamAccuracy()
strategy = Naive(
model,
optimizer,
criterion,
train_epochs=2,
eval_every=-1,
evaluator=EvaluationPlugin(acc),
)
strategy.train(benchmark.train_stream[0])
# eval is not called in this case
assert len(strategy.evaluator.get_all_metrics()) == 0
###################
# Case #2: Eval at the end only and before training
###################
acc = StreamAccuracy()
evalp = EvaluationPlugin(acc)
strategy = Naive(
model,
optimizer,
criterion,
train_epochs=2,
eval_every=0,
evaluator=evalp,
)
strategy.train(benchmark.train_stream[0])
# eval is called once at the end of the training loop
curve = strategy.evaluator.get_all_metrics()[curve_key][1]
assert len(curve) == 2
###################
# Case #3: Eval after every epoch and before training
###################
acc = StreamAccuracy()
strategy = Naive(
model,
optimizer,
criterion,
train_epochs=2,
eval_every=1,
evaluator=EvaluationPlugin(acc),
)
strategy.train(benchmark.train_stream[0])
curve = strategy.evaluator.get_all_metrics()[curve_key][1]
assert len(curve) == 3
###################
# Case #4: Eval in iteration mode
###################
acc = StreamAccuracy()
strategy = Naive(
model,
optimizer,
criterion,
train_epochs=2,
eval_every=100,
evaluator=EvaluationPlugin(acc),
peval_mode="iteration",
)
strategy.train(benchmark.train_stream[0])
curve = strategy.evaluator.get_all_metrics()[curve_key][1]
assert len(curve) == 5
def main(args):
# --- CONFIG
device = torch.device(f"cuda:{args.cuda}"
if torch.cuda.is_available() and
args.cuda >= 0 else "cpu")
# ---------
# --- TRANSFORMATIONS
train_transform = transforms.Compose([
RandomCrop(28, padding=4),
ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
test_transform = transforms.Compose([
ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
# ---------
# --- SCENARIO CREATION
mnist_train = MNIST('./data/mnist', train=True,
download=True, transform=train_transform)
mnist_test = MNIST('./data/mnist', train=False,
download=True, transform=test_transform)
scenario = nc_scenario(
mnist_train, mnist_test, 5, task_labels=False, seed=1234)
# ---------
# MODEL CREATION
model = SimpleMLP(num_classes=scenario.n_classes)
interactive_logger = InteractiveLogger()
wandb_logger = WandBLogger(init_kwargs={"project": args.project, "name": args.run})
eval_plugin = EvaluationPlugin(
accuracy_metrics(
minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True),
loss_metrics(
minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True),
forgetting_metrics(experience=True, stream=True),
StreamConfusionMatrix(),
cpu_usage_metrics(
minibatch=True, epoch=True, experience=True, stream=True),
timing_metrics(
minibatch=True, epoch=True, experience=True, stream=True),
ram_usage_metrics(
every=0.5, minibatch=True, epoch=True, experience=True,
stream=True),
gpu_usage_metrics(
args.cuda, every=0.5, minibatch=True, epoch=True,
experience=True, stream=True),
disk_usage_metrics(
minibatch=True, epoch=True, experience=True, stream=True),
MAC_metrics(
minibatch=True, epoch=True, experience=True),
loggers=[interactive_logger, wandb_logger]
)
# CREATE THE STRATEGY INSTANCE (NAIVE)
cl_strategy = Naive(
model, SGD(model.parameters(), lr=0.001, momentum=0.9),
CrossEntropyLoss(), train_mb_size=100, train_epochs=4, eval_mb_size=100,
device=device, evaluator=eval_plugin)
# TRAINING LOOP
print('Starting experiment...')
results = []
for experience in scenario.train_stream:
print("Start of experience: ", experience.current_experience)
print("Current Classes: ", experience.classes_in_this_experience)
cl_strategy.train(experience)
print('Training completed')
print('Computing accuracy on the whole test set')
results.append(cl_strategy.eval(scenario.test_stream))
def run_base(experience, device, use_interactive_logger: bool = False):
"""
Runs Naive (from BaseStrategy) for one experience.
"""
def create_sub_experience_list(experience):
"""Creates a list of sub-experiences from an experience.
It returns a list of experiences, where each experience is
a subset of the original experience.
:param experience: single Experience.
:return: list of Experience.
"""
# Shuffle the indices
indices = torch.randperm(len(experience.dataset))
num_sub_exps = len(indices)
mb_size = 1
sub_experience_list = []
for subexp_id in range(num_sub_exps):
subexp_indices = indices[subexp_id * mb_size:(subexp_id + 1) *
mb_size]
sub_experience = copy.copy(experience)
subexp_ds = AvalancheSubset(sub_experience.dataset,
indices=subexp_indices)
sub_experience.dataset = subexp_ds
sub_experience_list.append(sub_experience)
return sub_experience_list
# Create list of loggers to be used
loggers = []
if use_interactive_logger:
interactive_logger = InteractiveLogger()
loggers.append(interactive_logger)
# Evaluation plugin
eval_plugin = EvaluationPlugin(
accuracy_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
loss_metrics(minibatch=True, epoch=True, experience=True, stream=True),
forgetting_metrics(experience=True),
loggers=loggers,
)
# Model
model = SimpleMLP(num_classes=10)
# Create OnlineNaive strategy
cl_strategy = Naive(
model,
torch.optim.SGD(model.parameters(), lr=0.01),
CrossEntropyLoss(),
train_mb_size=1,
device=device,
evaluator=eval_plugin,
)
start = time.time()
sub_experience_list = create_sub_experience_list(experience)
# !!! This is only for profiling purpose. This method may not work
# in practice for dynamic modules since the model adaptation step
# can go wrong.
# Train for each sub-experience
print("Running OnlineNaive ...")
for i, sub_experience in enumerate(sub_experience_list):
experience = sub_experience
cl_strategy.train(experience)
end = time.time()
duration = end - start
return duration