def test_SplitCifar10_benchmark_download_once(self):
global CIFAR10_DOWNLOADS
CIFAR10_DOWNLOADS = 0
benchmark = SplitCIFAR10(5)
self.assertEqual(5, len(benchmark.train_stream))
self.assertEqual(5, len(benchmark.test_stream))
self.assertEqual(1, CIFAR10_DOWNLOADS)
def test_SplitCifar10_scenario_download_once(self):
global CIFAR10_DOWNLOADS
CIFAR10_DOWNLOADS = 0
scenario = SplitCIFAR10(5)
self.assertEqual(5, len(scenario.train_stream))
self.assertEqual(5, len(scenario.test_stream))
self.assertEqual(1, CIFAR10_DOWNLOADS)
def load_ar1_scenario(self, fast_test=False):
"""
Returns a NC Scenario from a fake dataset of 10 classes, 5 experiences,
2 classes per experience. This toy scenario is intended
:param fast_test: if True loads fake data, MNIST otherwise.
"""
if fast_test:
n_samples_per_class = 50
dataset = make_classification(n_samples=10 * n_samples_per_class,
n_classes=10,
n_features=224 * 224 * 3,
n_informative=6,
n_redundant=0)
X = torch.from_numpy(dataset[0]).reshape(-1, 3, 224, 224).float()
y = torch.from_numpy(dataset[1]).long()
train_X, test_X, train_y, test_y = train_test_split(X,
y,
train_size=0.6,
shuffle=True,
stratify=y)
train_dataset = TensorDataset(train_X, train_y)
test_dataset = TensorDataset(test_X, test_y)
my_nc_scenario = nc_scenario(train_dataset,
test_dataset,
5,
task_labels=False)
else:
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, ))
])
my_nc_scenario = SplitCIFAR10(5,
train_transform=train_transform,
eval_transform=test_transform)
return my_nc_scenario
def main(cuda: int):
# --- CONFIG
device = torch.device(
f"cuda:{cuda}" if torch.cuda.is_available() else "cpu"
)
# --- SCENARIO CREATION
scenario = SplitCIFAR10(n_experiences=2, seed=42)
# ---------
# MODEL CREATION
model = SimpleMLP(num_classes=scenario.n_classes, input_size=196608 // 64)
# choose some metrics and evaluation method
eval_plugin = EvaluationPlugin(
accuracy_metrics(stream=True, experience=True),
images_samples_metrics(
on_train=True,
on_eval=True,
n_cols=10,
n_rows=10,
),
labels_repartition_metrics(
# image_creator=repartition_bar_chart_image_creator,
on_train=True,
on_eval=True,
),
loggers=[
TensorboardLogger(f"tb_data/{datetime.now()}"),
InteractiveLogger(),
],
)
# CREATE THE STRATEGY INSTANCE (NAIVE)
cl_strategy = Naive(
model,
Adam(model.parameters()),
train_mb_size=128,
train_epochs=1,
eval_mb_size=128,
device=device,
plugins=[ReplayPlugin(mem_size=1_000)],
evaluator=eval_plugin,
)
# TRAINING LOOP
for i, experience in enumerate(scenario.train_stream, 1):
cl_strategy.train(experience)
cl_strategy.eval(scenario.test_stream[:i])
def main(args):
# Device 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)
# ---------
# CREATE THE STRATEGY INSTANCE
cl_strategy = AR1(criterion=CrossEntropyLoss(), 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, num_workers=4)
print('Training completed')
print('Computing accuracy on the whole test set')
results.append(cl_strategy.eval(scenario.test_stream, num_workers=4))
def test_SplitCifar10_benchmark(self):
benchmark = SplitCIFAR10(5)
self.assertEqual(5, len(benchmark.train_stream))
self.assertEqual(5, len(benchmark.test_stream))
train_sz = 0
for experience in benchmark.train_stream:
self.assertIsInstance(experience, Experience)
train_sz += len(experience.dataset)
# Regression test for 575
load_experience_train_eval(experience)
self.assertEqual(50000, train_sz)
test_sz = 0
for experience in benchmark.test_stream:
self.assertIsInstance(experience, Experience)
test_sz += len(experience.dataset)
# Regression test for 575
load_experience_train_eval(experience)
self.assertEqual(10000, test_sz)
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),
ExperienceForgetting(),
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 main():
args = parser.parse_args()
args.cuda = args.cuda == 'yes'
args.disable_pbar = args.disable_pbar == 'yes'
args.stable_sgd = args.stable_sgd == 'yes'
print(f"args={vars(args)}")
device = torch.device("cuda:0" if torch.cuda.is_available() and args.cuda else "cpu")
print(f'Using device: {device}')
# unique identifier
uid = uuid.uuid4().hex if args.uid is None else args.uid
now = str(datetime.datetime.now().date()) + "_" + ':'.join(str(datetime.datetime.now().time()).split(':')[:-1])
runname = 'T={}_id={}'.format(now, uid) if not args.resume else args.resume
# Paths
setupname = [args.strategy, args.exp_name, args.model, args.scenario]
parentdir = os.path.join(args.save_path, '_'.join(setupname))
results_path = Path(os.path.join(parentdir, runname))
results_path.mkdir(parents=True, exist_ok=True)
tb_log_dir = os.path.join(results_path, 'tb_run') # Group all runs
# Eval results
eval_metric = 'Top1_Acc_Stream/eval_phase/test_stream'
eval_results_dir = results_path / eval_metric.split('/')[0]
eval_results_dir.mkdir(parents=True, exist_ok=True)
eval_result_files = [] # To avg over seeds
seeds = [args.seed] if args.seed is not None else list(range(args.n_seeds))
for seed in seeds:
# initialize seeds
print("STARTING SEED {}/{}".format(seed, len(seeds) - 1))
set_seed(seed)
# create scenario
if args.scenario == 'smnist':
inputsize = 28 * 28
scenario = SplitMNIST(n_experiences=5, return_task_id=False, seed=seed,
fixed_class_order=[i for i in range(10)])
elif args.scenario == 'CIFAR10':
scenario = SplitCIFAR10(n_experiences=5, return_task_id=False, seed=seed,
fixed_class_order=[i for i in range(10)])
inputsize = (3, 32, 32)
elif args.scenario == 'miniimgnet':
scenario = SplitMiniImageNet(args.dset_rootpath, n_experiences=20, return_task_id=False, seed=seed,
fixed_class_order=[i for i in range(100)])
inputsize = (3, 84, 84)
else:
raise ValueError("Wrong scenario name.")
print(f"Scenario = {args.scenario}")
if args.model == 'simple_mlp':
model = MyMLP(input_size=inputsize, hidden_size=args.hs)
elif args.model == 'resnet18':
if not args.stable_sgd:
assert args.drop_prob == 0
model = ResNet18(inputsize, scenario.n_classes, drop_prob=args.drop_prob)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
# Paths
eval_results_file = eval_results_dir / f'seed={seed}.csv'
# LOGGING
tb_logger = TensorboardLogger(tb_log_dir=tb_log_dir, tb_log_exp_name=f'seed={seed}.pt') # log to Tensorboard
print_logger = TextLogger() if args.disable_pbar else InteractiveLogger() # print to stdout
eval_logger = EvalTextLogger(metric_filter=eval_metric, file=open(eval_results_file, 'a'))
eval_result_files.append(eval_results_file)
# METRICS
eval_plugin = EvaluationPlugin(
accuracy_metrics(experience=True, stream=True),
loss_metrics(minibatch=True, experience=True),
ExperienceForgetting(), # Test only
StreamConfusionMatrix(num_classes=scenario.n_classes, save_image=True),
# LOG OTHER STATS
# timing_metrics(epoch=True, experience=False),
# cpu_usage_metrics(experience=True),
# DiskUsageMonitor(),
# MinibatchMaxRAM(),
# GpuUsageMonitor(0),
loggers=[print_logger, tb_logger, eval_logger])
plugins = None
if args.strategy == 'replay':
plugins = [RehRevPlugin(n_total_memories=args.mem_size,
mode=args.replay_mode, # STEP-BACK
aversion_steps=args.aversion_steps,
aversion_lr=args.aversion_lr,
stable_sgd=args.stable_sgd, # Stable SGD
lr_decay=args.lr_decay,
init_epochs=args.init_epochs # First task epochs
)]
# CREATE THE STRATEGY INSTANCE (NAIVE)
strategy = Naive(model, optimizer, criterion,
train_epochs=args.epochs, device=device,
train_mb_size=args.bs, evaluator=eval_plugin,
plugins=plugins
)
# train on the selected scenario with the chosen strategy
print('Starting experiment...')
for experience in scenario.train_stream:
if experience.current_experience == args.until_task:
print("CUTTING OF TRAINING AT TASK ", experience.current_experience)
break
else:
print("Start training on step ", experience.current_experience)
strategy.train(experience)
print("End training on step ", experience.current_experience)
print('Computing accuracy on the test set')
res = strategy.eval(scenario.test_stream[:args.until_task]) # Gathered by EvalLogger
final_results_file = eval_results_dir / f'seed_summary.pt'
stat_summarize(eval_result_files, final_results_file)
print(f"[FILE:TB-RESULTS]: {tb_log_dir}")
print(f"[FILE:FINAL-RESULTS]: {final_results_file}")
print("FINISHED SCRIPT")