def test_joint(self):
class JointSTestPlugin(SupervisedPlugin):
def __init__(self, benchmark):
super().__init__()
self.benchmark = benchmark
def after_train_dataset_adaptation(
self, strategy: "SupervisedTemplate", **kwargs
):
"""
Check that the dataset used for training contains the
correct number of samples.
"""
cum_len = sum(
[len(exp.dataset) for exp in self.benchmark.train_stream]
)
assert len(strategy.adapted_dataset) == cum_len
# SIT scenario
model, optimizer, criterion, my_nc_benchmark = self.init_sit()
strategy = JointTraining(
model,
optimizer,
criterion,
train_mb_size=64,
device=self.device,
eval_mb_size=50,
train_epochs=2,
plugins=[JointSTestPlugin(my_nc_benchmark)],
)
strategy.evaluator.loggers = [TextLogger(sys.stdout)]
strategy.train(my_nc_benchmark.train_stream)
# MT scenario
my_nc_benchmark = self.load_benchmark(use_task_labels=True)
strategy = JointTraining(
model,
optimizer,
criterion,
train_mb_size=64,
device=self.device,
eval_mb_size=50,
train_epochs=2,
plugins=[JointSTestPlugin(my_nc_benchmark)],
)
strategy.evaluator.loggers = [TextLogger(sys.stdout)]
strategy.train(my_nc_benchmark.train_stream)
# Raise error when retraining
self.assertRaises(
AlreadyTrainedError,
lambda: strategy.train(my_nc_benchmark.train_stream),
)
def test_multihead_head_selection(self):
# Check if the optimizer is updated correctly
# when heads are created and updated.
model = MultiHeadClassifier(in_features=6)
optimizer = SGD(model.parameters(), lr=1e-3)
criterion = CrossEntropyLoss()
benchmark = get_fast_benchmark(use_task_labels=True, shuffle=False)
strategy = Naive(model, optimizer, criterion,
train_mb_size=100, train_epochs=1,
eval_mb_size=100, device='cpu')
strategy.evaluator.loggers = [TextLogger(sys.stdout)]
# initialize head
strategy.train(benchmark.train_stream[0])
strategy.train(benchmark.train_stream[4])
# create models with fixed head
model_t0 = model.classifiers['0']
model_t4 = model.classifiers['4']
# check head task0
for x, y, t in DataLoader(benchmark.train_stream[0].dataset):
y_mh = model(x, t)
y_t = model_t0(x)
assert ((y_mh - y_t) ** 2).sum() < 1.e-7
break
# check head task4
for x, y, t in DataLoader(benchmark.train_stream[4].dataset):
y_mh = model(x, t)
y_t = model_t4(x)
assert ((y_mh - y_t) ** 2).sum() < 1.e-7
break
def test_incremental_classifier_weight_update(self):
model = IncrementalClassifier(in_features=10)
optimizer = SGD(model.parameters(), lr=1e-3)
criterion = CrossEntropyLoss()
scenario = self.scenario
strategy = Naive(model,
optimizer,
criterion,
train_mb_size=100,
train_epochs=1,
eval_mb_size=100,
device='cpu')
strategy.evaluator.loggers = [TextLogger(sys.stdout)]
# train on first task
w_old = model.classifier.weight.clone()
b_old = model.classifier.bias.clone()
# adaptation. Increase number of classes
dataset = scenario.train_stream[4].dataset
model.adaptation(dataset)
w_new = model.classifier.weight.clone()
b_new = model.classifier.bias.clone()
# old weights should be copied correctly.
assert torch.equal(w_old, w_new[:w_old.shape[0]])
assert torch.equal(b_old, b_new[:w_old.shape[0]])
# shape should be correct.
assert w_new.shape[0] == max(dataset.targets) + 1
assert b_new.shape[0] == max(dataset.targets) + 1
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()
scenario = self.scenario
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: ",
scenario.train_stream[0].classes_in_this_experience)
print("Current Classes: ",
scenario.train_stream[4].classes_in_this_experience)
# train on first task
strategy.train(scenario.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(scenario.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(scenario.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
def test_multihead_head_creation(self):
# Check if the optimizer is updated correctly
# when heads are created and updated.
model = MTSimpleMLP(input_size=6, hidden_size=10)
optimizer = SGD(model.parameters(), lr=1e-3)
criterion = CrossEntropyLoss()
benchmark = get_fast_benchmark(use_task_labels=True, shuffle=False)
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[4].classes_in_this_experience,
)
print(
"Current Classes: ",
benchmark.train_stream[0].classes_in_this_experience,
)
# head creation
strategy.train(benchmark.train_stream[0])
w_ptr = model.classifier.classifiers["0"].classifier.weight.data_ptr()
b_ptr = model.classifier.classifiers["0"].classifier.bias.data_ptr()
opt_params_ptrs = [
w.data_ptr() for group in optimizer.param_groups
for w in group["params"]
]
assert w_ptr in opt_params_ptrs
assert b_ptr in opt_params_ptrs
# head update
strategy.train(benchmark.train_stream[4])
w_ptr_t0 = model.classifier.classifiers[
"0"].classifier.weight.data_ptr()
b_ptr_t0 = model.classifier.classifiers["0"].classifier.bias.data_ptr()
w_ptr_new = model.classifier.classifiers[
"4"].classifier.weight.data_ptr()
b_ptr_new = model.classifier.classifiers["4"].classifier.bias.data_ptr(
)
opt_params_ptrs = [
w.data_ptr() for group in optimizer.param_groups
for w in group["params"]
]
assert w_ptr not in opt_params_ptrs # head0 has been updated
assert b_ptr not in opt_params_ptrs # head0 has been updated
assert w_ptr_t0 in opt_params_ptrs
assert b_ptr_t0 in opt_params_ptrs
assert w_ptr_new in opt_params_ptrs
assert b_ptr_new in opt_params_ptrs
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()
text_logger = TextLogger(open('log.txt', 'a'))
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
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 run_strategy(self, benchmark, cl_strategy):
print("Starting experiment...")
cl_strategy.evaluator.loggers = [TextLogger(sys.stdout)]
results = []
for train_batch_info in benchmark.train_stream:
print("Start of experience ", train_batch_info.current_experience)
cl_strategy.train(train_batch_info, num_workers=0)
print("Training completed")
print("Computing accuracy on the current test set")
results.append(cl_strategy.eval(benchmark.test_stream[:]))
def run_strategy(self, scenario, cl_strategy):
print('Starting experiment...')
cl_strategy.evaluator.loggers = [TextLogger(sys.stdout)]
results = []
for train_batch_info in scenario.train_stream:
print("Start of experience ", train_batch_info.current_experience)
cl_strategy.train(train_batch_info)
print('Training completed')
print('Computing accuracy on the current test set')
results.append(cl_strategy.eval(scenario.test_stream[:]))
def test_multihead_optimizer_update(self):
# Check if the optimizer is updated correctly
# when heads are created and updated.
model = SimpleMLP(input_size=6, hidden_size=10)
optimizer = SGD(model.parameters(), lr=1e-3)
criterion = CrossEntropyLoss()
scenario = self.create_scenario()
plug = MultiHeadPlugin(model, 'classifier')
strategy = Naive(model,
optimizer,
criterion,
train_mb_size=100,
train_epochs=1,
eval_mb_size=100,
device='cpu',
plugins=[plug])
strategy.evaluator.loggers = [TextLogger(sys.stdout)]
print("Current Classes: ",
scenario.train_stream[0].classes_in_this_experience)
print("Current Classes: ",
scenario.train_stream[4].classes_in_this_experience)
# head creation
strategy.train(scenario.train_stream[0])
w_ptr = model.classifier.weight.data_ptr()
b_ptr = model.classifier.bias.data_ptr()
opt_params_ptrs = [
w.data_ptr() for group in optimizer.param_groups
for w in group['params']
]
assert w_ptr in opt_params_ptrs
assert b_ptr in opt_params_ptrs
# head update
strategy.train(scenario.train_stream[4])
w_ptr_new = model.classifier.weight.data_ptr()
b_ptr_new = model.classifier.bias.data_ptr()
opt_params_ptrs = [
w.data_ptr() for group in optimizer.param_groups
for w in group['params']
]
assert w_ptr not in opt_params_ptrs
assert b_ptr not in opt_params_ptrs
assert w_ptr_new in opt_params_ptrs
assert b_ptr_new in opt_params_ptrs
def test_callback_reachability(self):
# Check that all the callbacks are called during
# training and test loops.
model = SimpleMLP(input_size=6, hidden_size=10)
optimizer = SGD(model.parameters(), lr=1e-3)
criterion = CrossEntropyLoss()
scenario = self.create_scenario()
plug = MockPlugin()
strategy = Naive(model, optimizer, criterion,
train_mb_size=100, train_epochs=1, eval_mb_size=100,
device='cpu', plugins=[plug]
)
strategy.evaluator.loggers = [TextLogger(sys.stdout)]
strategy.train(scenario.train_stream[0], num_workers=4)
strategy.eval([scenario.test_stream[0]], num_workers=4)
assert all(plug.activated)
def test_callback_reachability(self):
# Check that all the callbacks are called during
# training and test loops.
model = _PlainMLP(input_size=6, hidden_size=10)
optimizer = SGD(model.parameters(), lr=1e-3)
criterion = CrossEntropyLoss()
benchmark = PluginTests.create_benchmark()
plug = MockPlugin()
strategy = Naive(
model,
optimizer,
criterion,
train_mb_size=100,
train_epochs=1,
eval_mb_size=100,
device="cpu",
plugins=[plug],
)
strategy.evaluator.loggers = [TextLogger(sys.stdout)]
strategy.train(benchmark.train_stream[0], num_workers=0)
strategy.eval([benchmark.test_stream[0]], num_workers=0)
assert all(plug.activated)
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())}")
generic_scenario = tensor_scenario(
train_data_x=dataset[0],
train_data_y=dataset[1],
test_data_x=dataset[2],
test_data_y=dataset[3],
task_labels=[0 for key in task_order_list[i].keys()
], # shouldn't provide task ID for inference
)
# log to Tensorboard
tb_logger = TensorboardLogger(
f"./tb_data/{cur_time}_CNN1D_0inTask{task_order}/")
# log to text file
text_logger = TextLogger(
open(f"./logs/{cur_time}_CNN1D_0inTask{task_order}.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),
ExperienceForgetting(),
cpu_usage_metrics(experience=True),
StreamConfusionMatrix(num_classes=2, save_image=False),
disk_usage_metrics(minibatch=True,
generic_scenario = tensor_scenario(
train_data_x=dataset[0],
train_data_y=dataset[1],
test_data_x=dataset[2],
test_data_y=dataset[3],
task_labels=[
0 for key in task_order_list[0].keys()
], # shouldn't provide task ID for inference
)
# log to Tensorboard
tb_logger = TensorboardLogger(f"./tb_data/{cur_time}-SimpleMLP/")
# log to text file
text_logger = TextLogger(open(f"./logs/{cur_time}-SimpleMLP.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),
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],
)
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 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 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):
"""
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 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")
train_data_y=dataset[1],
test_data_x=dataset[2],
test_data_y=dataset[3],
task_labels=[
0 for key in task_order_list[task_order].keys()
], # shouldn't provide task ID for inference
)
# log to Tensorboard
tb_logger = TensorboardLogger(
f"./tb_data/{cur_time}_simple_mlp_task_0_in_task_{task_order}/"
)
# log to text file
text_logger = TextLogger(
open(
f"./logs/{cur_time}_simple_mlp_task_0_in_task{task_order}.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),
ExperienceForgetting(),
cpu_usage_metrics(experience=True),
StreamConfusionMatrix(num_classes=2, save_image=False),
disk_usage_metrics(minibatch=True, epoch=True,
experience=True, stream=True),
normalize,
])
test_transform = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])
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,
test_datasets)
scenario = scenario_custom_task_labels
if (args.grow_classifier):
print("using incremental classifier")
model = DCNNNoVAEIncremental(num_classes=2)
else:
model = DCNNNoVAE(num_classes=5) # 5 because I happen to know this
# log to Tensorboard
path = args.tb_log_dir + "/" + ("".join(str(
datetime.now()).split(" "))) + "_" + args.cl_strategy
tb_logger = TensorboardLogger(tb_log_dir=path)
# log to text file
text_logger = TextLogger(open('log.txt', 'a'))
# print to stdout
interactive_logger = InteractiveLogger()
eval_plugin = EvaluationPlugin(
accuracy_metrics(minibatch=False,
epoch=False,
experience=True,
stream=True),
#loss_metrics(minibatch=True, epoch=True, experience=True, stream=True),
#timing_metrics(epoch=True),
#cpu_usage_metrics(experience=True),
#forgetting_metrics(experience=True, stream=True),
#StreamConfusionMatrix(num_classes=5, save_image=False),
StreamConfusionMatrix(save_image=False),
#disk_usage_metrics(minibatch=True, epoch=True, experience=True, stream=True)
loggers=[interactive_logger, text_logger, tb_logger])
train_data_x=dataset[0],
train_data_y=dataset[1],
test_data_x=dataset[2],
test_data_y=dataset[3],
task_labels=[0 for key in perm.keys()],
)
# Model Creation
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = SimpleMLP(num_classes=2, input_size=70, hidden_size=100)
# log to Tensorboard
tb_logger = TensorboardLogger(f"./tb_data/{cur_time}-simpleMLP_Domain/")
# log to text file
text_logger = TextLogger(open(f"./logs/{cur_time}-simpleMLP_Domain.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),
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],
)
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 test_text_logger(self):
logp = TextLogger()
self._test_logger(logp)
generic_scenario = tensor_scenario(
train_data_x=dataset[0],
train_data_y=dataset[1],
test_data_x=dataset[2],
test_data_y=dataset[3],
task_labels=[0 for key in task_order_list[task_order].keys()
], # shouldn't provide task ID for inference
)
# log to Tensorboard
tb_logger = TensorboardLogger(
f"./tb_data/{cur_time}_CNN2D_ClassInc_0_in_task{task_order+1}/")
# log to text file
text_logger = TextLogger(
open(f"./logs/{cur_time}_CNN2D_ClassInc_0_in_task{task_order+1}.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),
ExperienceForgetting(),
cpu_usage_metrics(experience=True),
StreamConfusionMatrix(num_classes=2, save_image=False),
generic_scenario = tensor_scenario(
train_data_x=dataset[0],
train_data_y=dataset[1],
test_data_x=dataset[2],
test_data_y=dataset[3],
task_labels=[
0 for key in task_order_list[task_order].keys()
], # shouldn't provide task ID for inference
)
# log to Tensorboard
tb_logger = TensorboardLogger(f"./tb_data/{cur_time}_CNN2D_0task_{task_order+1}/")
# log to text file
text_logger = TextLogger(
open(f"./logs/{cur_time}_CNN2D_0task_{task_order+1}.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),
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],
)
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)
