def main(args):
# Model getter: specify dataset and depth of the network.
model = pytorchcv_wrapper.resnet('cifar10', depth=20, pretrained=False)
# Or get a more specific model. E.g. wide resnet, with depth 40 and growth
# factor 8 for Cifar 10.
# model = pytorchcv_wrapper.get_model("wrn40_8_cifar10", pretrained=False)
# --- CONFIG
device = torch.device(f"cuda:{args.cuda}"
if torch.cuda.is_available() and
args.cuda >= 0 else "cpu")
device = "cpu"
# --- TRANSFORMATIONS
transform = transforms.Compose([
ToTensor(),
transforms.Normalize((0.491, 0.482, 0.446), (0.247, 0.243, 0.261))
])
# --- SCENARIO CREATION
cifar_train = CIFAR10(root=expanduser("~") + "/.avalanche/data/cifar10/",
train=True, download=True, transform=transform)
cifar_test = CIFAR10(root=expanduser("~") + "/.avalanche/data/cifar10/",
train=False, download=True, transform=transform)
scenario = nc_benchmark(
cifar_train, cifar_test, 5, task_labels=False, seed=1234,
fixed_class_order=[i for i in range(10)])
# 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, with Replay)
cl_strategy = Naive(model, torch.optim.SGD(model.parameters(), lr=0.01),
CrossEntropyLoss(),
train_mb_size=100, train_epochs=1, eval_mb_size=100,
device=device,
plugins=[ReplayPlugin(mem_size=1000)],
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 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 Permuted MNIST scenario
scenario = PermutedMNIST(n_experiences=4)
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 strategy
assert (
len(args.lambda_e) == 1 or len(args.lambda_e) == 5
), "Lambda_e must be a non-empty list."
lambda_e = args.lambda_e[0] if len(args.lambda_e) == 1 else args.lambda_e
strategy = LFL(
model,
optimizer,
criterion,
lambda_e=lambda_e,
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 train_batch_info in scenario.train_stream:
print(
"Start training on experience ", train_batch_info.current_experience
)
strategy.train(train_batch_info, num_workers=0)
print(
"End training on experience ", train_batch_info.current_experience
)
print("Computing accuracy on the test set")
results.append(strategy.eval(scenario.test_stream[:]))
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),
forgetting_metrics(experience=True),
loggers=[interactive_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):
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 split scenario
scenario = SplitMNIST(n_experiences=5, return_task_id=False)
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 strategy
assert len(args.lwf_alpha) == 1 or len(args.lwf_alpha) == 5,\
'Alpha must be a non-empty list.'
lwf_alpha = args.lwf_alpha[0] if len(args.lwf_alpha) == 1 \
else args.lwf_alpha
strategy = LwF(model,
optimizer,
criterion,
alpha=lwf_alpha,
temperature=args.softmax_temperature,
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 train_batch_info in scenario.train_stream:
print("Start training on experience ",
train_batch_info.current_experience)
strategy.train(train_batch_info, num_workers=0)
print("End training on experience ",
train_batch_info.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")
# --- SCENARIO CREATION
scenario = SplitMNIST(n_experiences=10, 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 (GenerativeReplay)
cl_strategy = GenerativeReplay(
model,
torch.optim.Adam(model.parameters(), lr=0.001),
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)
cl_strategy.train(experience)
print("Training completed")
print("Computing accuracy on the whole test set")
results.append(cl_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")
# model
model = MTSimpleMLP()
# CL Benchmark Creation
scenario = SplitMNIST(n_experiences=5, return_task_id=True)
train_stream = scenario.train_stream
test_stream = scenario.test_stream
# Prepare for training & testing
optimizer = Adam(model.parameters(), lr=0.01)
criterion = CrossEntropyLoss()
# choose some metrics and evaluation method
interactive_logger = InteractiveLogger()
eval_plugin = EvaluationPlugin(
accuracy_metrics(minibatch=False,
epoch=True,
experience=True,
stream=True),
forgetting_metrics(experience=True),
loggers=[interactive_logger],
)
# Choose a CL strategy
strategy = EWC(
model=model,
optimizer=optimizer,
criterion=criterion,
train_mb_size=128,
train_epochs=3,
eval_mb_size=128,
device=device,
evaluator=eval_plugin,
ewc_lambda=0.4,
)
# train and test loop
for train_task in train_stream:
strategy.train(train_task)
strategy.eval(test_stream)
def run_base_online(experience, device, use_interactive_logger: bool = False):
"""
Runs OnlineNaive for one experience.
"""
# 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 = OnlineNaive(
model,
torch.optim.SGD(model.parameters(), lr=0.01),
CrossEntropyLoss(),
num_passes=1,
train_mb_size=1,
device=device,
evaluator=eval_plugin,
)
start = time.time()
print("Running OnlineNaive ...")
cl_strategy.train(experience)
end = time.time()
duration = end - start
return duration
def main(args):
# Device config
device = torch.device(f"cuda:{args.cuda}"
if torch.cuda.is_available() and
args.cuda >= 0 else "cpu")
print('device ', device)
# ---------
# --- TRANSFORMATIONS
_mu = [0.485, 0.456, 0.406] # imagenet normalization
_std = [0.229, 0.224, 0.225]
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=_mu,
std=_std)
])
# ---------
# --- SCENARIO CREATION
scenario = CORe50(scenario=args.scenario, train_transform=transform,
eval_transform=transform)
# ---------
eval_plugin = EvaluationPlugin(
loss_metrics(epoch=True, experience=True, stream=True),
accuracy_metrics(epoch=True, experience=True, stream=True),
forgetting_metrics(experience=True, stream=True),
loggers=[InteractiveLogger()]
)
criterion = torch.nn.CrossEntropyLoss()
model = SLDAResNetModel(device=device, arch='resnet18',
imagenet_pretrained=args.imagenet_pretrained)
# CREATE THE STRATEGY INSTANCE
cl_strategy = StreamingLDA(model, criterion,
args.feature_size, args.n_classes,
eval_mb_size=args.batch_size,
train_mb_size=args.batch_size,
train_epochs=1,
shrinkage_param=args.shrinkage,
streaming_update_sigma=args.plastic_cov,
device=device, evaluator=eval_plugin)
warnings.warn(
"The Deep SLDA example is not perfectly aligned with "
"the paper implementation since it does not use a base "
"initialization phase and instead starts streming from "
"pre-trained weights.")
# TRAINING LOOP
print('Starting experiment...')
for i, exp in enumerate(scenario.train_stream):
# fit SLDA model to batch (one sample at a time)
cl_strategy.train(exp)
# evaluate model on test data
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
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):
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"
)
# --- SCENARIO CREATION
scenario = SplitCIFAR100(n_experiences=20, return_task_id=True)
config = {"scenario": "SplitCIFAR100"}
# MODEL CREATION
model = MTSimpleCNN()
# choose some metrics and evaluation method
loggers = [InteractiveLogger()]
if args.wandb_project != "":
wandb_logger = WandBLogger(
project_name=args.wandb_project,
run_name="LaMAML_" + config["scenario"],
config=config,
)
loggers.append(wandb_logger)
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,
)
# LAMAML STRATEGY
rs_buffer = ReservoirSamplingBuffer(max_size=200)
replay_plugin = ReplayPlugin(
mem_size=200,
batch_size=10,
batch_size_mem=10,
task_balanced_dataloader=False,
storage_policy=rs_buffer,
)
cl_strategy = LaMAML(
model,
torch.optim.SGD(model.parameters(), lr=0.1),
CrossEntropyLoss(),
n_inner_updates=5,
second_order=True,
grad_clip_norm=1.0,
learn_lr=True,
lr_alpha=0.25,
sync_update=False,
train_mb_size=10,
train_epochs=10,
eval_mb_size=100,
device=device,
plugins=[replay_plugin],
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))
if args.wandb_project != "":
wandb.finish()
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())}")
root = ".."
# log to Tensorboard
tb_logger = TensorboardLogger(root)
# log to text file
text_logger = TextLogger(open(f"{root}/log.txt", "w"))
# 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),
timing_metrics(epoch=True, epoch_running=True),
forgetting_metrics(experience=True, stream=True),
cpu_usage_metrics(experience=True),
confusion_matrix_metrics(num_classes=NUM_CLASSES[data_name],
save_image=False,
stream=True),
disk_usage_metrics(minibatch=True,
epoch=True,
experience=True,
stream=True),
loggers=[interactive_logger, text_logger, tb_logger],
)
num_epoch = 70
for eval_mode in EVALUATION_PROTOCOLS:
for mode in ["feature", "image"]:
if eval_mode == "streaming":
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)
interactive_logger = InteractiveLogger()
wandb_logger = WandBLogger(project_name=args.project,
run_name=args.run,
config=vars(args))
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),
confusion_matrix_metrics(stream=True,
wandb=True,
class_names=[str(i) for i in range(10)]),
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 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=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],
shuffle=False)
cl_strategy.eval(benchmark.test_stream)
cls.all_metrics = cl_strategy.evaluator.get_all_metrics()
f.close()
# # Uncomment me to regenerate the reference metrics. Make sure
# # the old tests were passing for all unchanged 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):
# --- CONFIG
device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available()
and args.cuda >= 0 else "cpu")
# ---------
# --- TRANSFORMATIONS
train_transform = transforms.Compose([
Resize(224),
ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
test_transform = transforms.Compose([
Resize(224),
ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
# ---------
# --- SCENARIO CREATION
scenario = SplitCIFAR10(5,
train_transform=train_transform,
eval_transform=test_transform)
# ---------
# MODEL CREATION
model = MobilenetV1()
adapt_classification_layer(model, scenario.n_classes, bias=False)
# DEFINE THE EVALUATION PLUGIN AND LOGGER
my_logger = TensorboardLogger(tb_log_dir="logs",
tb_log_exp_name="logging_example")
# print to stdout
interactive_logger = InteractiveLogger()
evaluation_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=[my_logger, interactive_logger],
)
# CREATE THE STRATEGY INSTANCE (NAIVE with the Synaptic Intelligence plugin)
cl_strategy = SynapticIntelligence(
model,
Adam(model.parameters(), lr=0.001),
CrossEntropyLoss(),
si_lambda=0.0001,
train_mb_size=128,
train_epochs=4,
eval_mb_size=128,
device=device,
evaluator=evaluation_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 setUpClass(cls) -> None:
torch.manual_seed(0)
np.random.seed(0)
random.seed(0)
n_samples_per_class = 100
dataset = make_classification(n_samples=6 * n_samples_per_class,
n_classes=6,
n_features=4,
n_informative=4,
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)
tr_d = TensorDataset(train_X, train_y)
ts_d = TensorDataset(test_X, test_y)
benchmark = nc_benchmark(train_dataset=tr_d,
test_dataset=ts_d,
n_experiences=3,
task_labels=True,
shuffle=False,
seed=0)
model = SimpleMLP(input_size=4, num_classes=benchmark.n_classes)
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=6,
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=10,
train_epochs=2,
eval_mb_size=10,
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',
# 'mt.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',
'mt.pickle'), 'rb') as f:
cls.ref = pickle.load(f)
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 (ONLINE-NAIVE)
cl_strategy = OnlineNaive(
model,
torch.optim.Adam(model.parameters(), lr=0.001),
CrossEntropyLoss(),
num_passes=1,
train_mb_size=1,
eval_mb_size=32,
device=device,
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 setUpClass(cls) -> None:
torch.manual_seed(0)
np.random.seed(0)
random.seed(0)
n_samples_per_class = 100
dataset = make_classification(
n_samples=6 * n_samples_per_class,
n_classes=6,
n_features=4,
n_informative=4,
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)
tr_d = TensorDataset(train_X, train_y)
ts_d = TensorDataset(test_X, test_y)
benchmark = nc_benchmark(
train_dataset=tr_d,
test_dataset=ts_d,
n_experiences=3,
task_labels=True,
shuffle=False,
seed=0,
)
model = SimpleMLP(input_size=4, num_classes=benchmark.n_classes)
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=6,
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=10,
train_epochs=2,
eval_mb_size=10,
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",
"mt.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",
"mt.pickle",
),
"rb",
) as f:
cls.ref = pickle.load(f)
