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,
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),
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),
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"
)
# ---------
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())}")