def test_slicing_list_path_array(list_tasks):
x_train, y_train = gen_string()
dummy = InMemoryDataset(x_train, y_train, data_type=TaskType.IMAGE_PATH)
scenario = ClassIncremental(dummy, increment=1)
subscenario = create_subscenario(scenario, list_tasks)
assert subscenario.nb_tasks == len(list_tasks), print(
f"{len(subscenario)} - vs - {len(list_tasks)}")
def test_slicing_list(list_tasks):
train = gen_data()
dummy = InMemoryDataset(*train)
scenario = ClassIncremental(dummy, increment=1)
subscenario = create_subscenario(scenario, list_tasks)
assert subscenario.nb_tasks == len(list_tasks), print(
f"{len(subscenario)} - vs - {len(list_tasks)}")
def sample(self, seed: int = None, nb_tasks: int = None) -> _BaseScenario:
# seed the generator
if seed is None:
seed = np.random.randint(10000)
# generate a random task order
task_order = self.get_task_order(seed, nb_tasks)
subscenario = create_subscenario(self.base_scenario,
task_order[:nb_tasks])
return subscenario
def test_Online_Fellowship_subscenarios(dataset7c, dataset10c, dataset20c):
scenario = OnlineFellowship([dataset7c, dataset10c, dataset20c])
sub_scenario = create_subscenario(scenario, np.arange(scenario.nb_tasks - 1))
for task_set in sub_scenario:
loader = DataLoader(task_set)
for _ in loader:
pass
assert sub_scenario.nb_tasks == scenario.nb_tasks - 1
task_order = np.arange(scenario.nb_tasks)
np.random.shuffle(task_order)
sub_scenario = create_subscenario(scenario, task_order)
for task_set in sub_scenario:
loader = DataLoader(task_set)
for _ in loader:
pass
assert sub_scenario.nb_tasks == scenario.nb_tasks
def test_h5dataset_reloading_slow(tmpdir):
filename_h5 = os.path.join(tmpdir, "test_h5.hdf5")
nb_tasks = 5
cl_dataset = CIFAR100(data_path=DATA_PATH,
download=False,
train=True,
labels_type="category",
task_labels="lifelong")
x, y, t = cl_dataset.get_data()
# create dataset
h5dataset = H5Dataset(x, y, t, data_path=filename_h5)
# destroy object
del h5dataset
# reload data set
h5dataset_reloaded = H5Dataset(x=None, y=None, t=None, data_path=filename_h5)
scenario = ContinualScenario(h5dataset_reloaded)
for task_set in scenario:
loader = DataLoader(task_set)
for _ in loader:
pass
assert scenario.nb_tasks == nb_tasks
task_order = np.arange(nb_tasks)
sub_scenario = create_subscenario(scenario, task_order[:-1])
assert sub_scenario.nb_tasks == nb_tasks-1
np.random.shuffle(task_order)
sub_scenario = create_subscenario(scenario, task_order)
assert sub_scenario.nb_tasks == nb_tasks
def test_sequence_transforms(list_tasks):
x_train, y_train, t_train = gen_data()
dummy = InMemoryDataset(x_train,
y_train,
t_train,
data_type=TaskType.IMAGE_PATH)
nb_task = len(np.unique(y_train))
list_trsfs = []
for _ in range(nb_task):
list_trsfs.append([transforms.RandomAffine(degrees=[0, 90])])
scenario = ClassIncremental(dummy, increment=1, transformations=list_trsfs)
subscenario = create_subscenario(scenario, list_tasks)
assert subscenario.nb_tasks == len(list_tasks), print(
f"{len(subscenario)} - vs - {len(list_tasks)}")
def test_create_subscenario_h5dataset(data, tmpdir):
from continuum.scenarios import create_subscenario
filename_h5 = os.path.join(tmpdir, "test_h5.hdf5")
x_, y_, t_ = data
h5dataset = H5Dataset(x_, y_, t_, data_path=filename_h5)
nb_task = len(np.unique(t_))
scenario = ContinualScenario(h5dataset)
sub_scenario = create_subscenario(scenario, np.arange(nb_task - 1))
for task_set in sub_scenario:
loader = DataLoader(task_set)
for _ in loader:
pass
assert sub_scenario.nb_tasks == nb_task - 1
def test_create_subscenario_suffle_h5dataset(data, tmpdir):
filename_h5 = os.path.join(tmpdir, "test_h5.hdf5")
x_, y_, t_ = data
h5dataset = H5Dataset(x_, y_, t_, data_path=filename_h5)
nb_task = len(np.unique(t_))
scenario = ContinualScenario(h5dataset)
task_order = np.arange(nb_task)
np.random.shuffle(task_order)
sub_scenario = create_subscenario(scenario, task_order)
for task_set in sub_scenario:
loader = DataLoader(task_set)
for _ in loader:
pass
assert sub_scenario.nb_tasks == nb_task
