def test_online_Fellowship_mixeddatatype(dataset10c, types, error):
datasets = []
for typ in types:
d = copy.deepcopy(dataset10c)
d._data_type = typ
d._nb_classes = 10
datasets.append(d)
if error:
with pytest.raises(ValueError):
scenario = OnlineFellowship(datasets)
else:
scenario = OnlineFellowship(datasets)
def test_online_Fellowship_transformation2(dataset7c, dataset10c, dataset20c):
sizes = [16, 24, 40]
transformations = [[trsf.Resize(size=(sizes[0], sizes[0])), trsf.ToTensor()],
[trsf.Resize(size=(sizes[1], sizes[1])), trsf.ToTensor()],
[trsf.Resize(size=(sizes[2], sizes[2])), trsf.ToTensor()]]
scenario = OnlineFellowship([dataset7c, dataset10c, dataset20c], transformations=transformations)
for task_id, taskset in enumerate(scenario):
loader = DataLoader(taskset)
x, _, _ = next(iter(loader))
assert x.shape[-1] == sizes[task_id]
def test_online_Fellowship_transformation(dataset7c, dataset10c, dataset20c, transformations):
scenario = OnlineFellowship([dataset7c, dataset10c, dataset20c], transformations=transformations)
assert len(scenario) == 3
tot_nb_classes = 0
for task_id, taskset in enumerate(scenario):
tot_nb_classes += taskset.nb_classes
loader = DataLoader(taskset)
_, _, _ = next(iter(loader))
assert tot_nb_classes == scenario.nb_classes
def test_Online_Fellowship(dataset7c, dataset10c, dataset20c):
scenario = OnlineFellowship([dataset7c, dataset10c, dataset20c])
for i, task_set in enumerate(scenario):
if i == 0:
assert task_set.nb_classes == 7
if i == 1:
assert task_set.nb_classes == 10
if i == 2:
assert task_set.nb_classes == 20
assert scenario[0].nb_classes == 7
assert scenario[1].nb_classes == 10
assert scenario[2].nb_classes == 20
def test_online_Fellowship_mix_path_array(list_datasets):
list_dict_args = [{"data_path": DATA_PATH, "train": True, "download": False}] * len(list_datasets)
list_instanciate_datasets = []
for i, dataset in enumerate(list_datasets):
list_instanciate_datasets.append(dataset(**list_dict_args[i]))
scenario = OnlineFellowship(list_instanciate_datasets, update_labels=True)
assert len(scenario) == len(list_datasets)
tot_nb_classes = 0
for task_id, taskset in enumerate(scenario):
tot_nb_classes += taskset.nb_classes
loader = DataLoader(taskset)
_, _, _ = next(iter(loader))
assert tot_nb_classes == scenario.nb_classes
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 create_subscenario(base_scenario, task_indexes):
"""
In this function we want to create a subscenario from the different tasks, either by subsampling tasks or reodering
or both.
:param base_scenario: scenario from which the subscenario will be created
:param task_indexes: array with new order of tasks
:return: A train PyTorch's Datasets.
"""
if torch.is_tensor(task_indexes):
task_indexes = task_indexes.numpy()
if base_scenario.transformations is not None and isinstance(
base_scenario.transformations[0], list):
transformations = [
base_scenario.transformations[i] for i in task_indexes
]
else:
transformations = base_scenario.transformations
sub_scenario = None
if isinstance(base_scenario, OnlineFellowship):
# We just want to changes base_scenario.cl_datasets order
new_cl_datasets = [base_scenario.cl_datasets[i] for i in task_indexes]
sub_scenario = OnlineFellowship(
new_cl_datasets,
transformations=transformations,
update_labels=base_scenario.update_labels)
elif base_scenario.cl_dataset.data_type == TaskType.H5:
list_taskset = [base_scenario[i] for i in task_indexes]
sub_scenario = OnlineFellowship(list_taskset,
transformations=transformations,
update_labels=False)
else:
new_x, new_y, new_t = None, None, None
if base_scenario.cl_dataset.bounding_boxes is not None:
raise ValueError(
"the function create_subscenario is not compatible with scenario with bounding_boxes yet."
)
for i, index in enumerate(task_indexes):
taskset = base_scenario[index]
all_task_indexes = np.arange(len(taskset))
x, y, t = taskset.get_raw_samples(all_task_indexes)
t = np.ones(len(y)) * i
if new_x is None:
new_x = x
new_y = y
new_t = t
else:
new_x = np.concatenate([new_x, x], axis=0)
new_y = np.concatenate([new_y, y], axis=0)
new_t = np.concatenate([new_t, t], axis=0)
dataset = InMemoryDataset(new_x,
new_y,
new_t,
data_type=base_scenario.cl_dataset.data_type)
sub_scenario = ContinualScenario(dataset,
transformations=transformations)
return sub_scenario